<?xml version="1.0" encoding="UTF-8"?>
 <rdf:RDF xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:cc="http://web.resource.org/cc/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:admin="http://webns.net/mvcb/">
  <channel rdf:about="http://pinboard.in">
    <title>Pinboard (cshalizi)</title>
    <link>https://pinboard.in/u:cshalizi/public/</link>
    <description>recent bookmarks from cshalizi</description>
    <items>
      <rdf:Seq>	<rdf:li rdf:resource="https://arxiv.org/abs/2210.16224"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2206.04902"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2202.06921"/>
	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/aer.20191432"/>
	<rdf:li rdf:resource="https://doi.org/10.1080/13563467.2021.1967910"/>
	<rdf:li rdf:resource="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-081020-044812"/>
	<rdf:li rdf:resource="https://www.rebuildingmacroeconomics.ac.uk/research-prize-complexity-macro"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2007.00273"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/2105.11182"/>
	<rdf:li rdf:resource="https://faculty.wcas.northwestern.edu/~gep575/PriorSelectionCovid2-3.pdf"/>
	<rdf:li rdf:resource="https://academic.oup.com/ectj/article-abstract/24/1/C33/5909595?redirectedFrom=fulltext"/>
	<rdf:li rdf:resource="https://arxiv.org/abs/1705.00395"/>
	<rdf:li rdf:resource="https://www.nber.org/papers/w23673"/>
	<rdf:li rdf:resource="https://link.springer.com/article/10.1007/s11238-012-9305-8"/>
	<rdf:li rdf:resource="https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00718"/>
	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/jep.32.3.141"/>
	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/jep.32.3.113"/>
	<rdf:li rdf:resource="https://www.aeaweb.org/articles?id=10.1257/jep.32.3.59"/>
	<rdf:li rdf:resource="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2684776"/>
	<rdf:li rdf:resource="http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00870"/>
	<rdf:li rdf:resource="http://krugman.blogs.nytimes.com/2016/08/12/the-state-of-macro-is-sad-wonkish/"/>
	<rdf:li rdf:resource="https://piie.com/system/files/documents/pb16-11.pdf"/>
	<rdf:li rdf:resource="http://press.princeton.edu/titles/10612.html"/>
	<rdf:li rdf:resource="http://public.econ.duke.edu/~kdh9/Source%20Materials/Research/Reductionism-Economics14May2015.pdf"/>
	<rdf:li rdf:resource="http://rjwaldmann.blogspot.com/2012/03/modern-macroeconomic-methodology-modern.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1410.3192"/>
	<rdf:li rdf:resource="http://www.voxeu.org/article/how-good-are-out-sample-forecasting-tests"/>
	<rdf:li rdf:resource="http://www.cims.nyu.edu/~vitaly/pub/fts.pdf"/>
	<rdf:li rdf:resource="http://www.voxeu.org/article/when-economic-models-are-unable-fit-data"/>
	<rdf:li rdf:resource="http://www.sciencedirect.com/science/article/pii/S0169207097000307"/>
	<rdf:li rdf:resource="http://equitablegrowth.org/2014/07/20/state-macroeconomics-good-monday-focus-july-21-2014/"/>
	<rdf:li rdf:resource="http://angrybearblog.com/2014/07/25621.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1406.2462"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1406.1037"/>
	<rdf:li rdf:resource="http://www.sciencedirect.com/science/article/pii/S0304407614000761"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/0707.0322"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1403.0740"/>
	<rdf:li rdf:resource="http://www.aeaweb.org/articles.php?doi=10.1257/aer.104.2.379"/>
	<rdf:li rdf:resource="http://noahpinionblog.blogspot.com/2014/01/the-equation-at-core-of-modern-macro.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1305.4825"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1401.0304"/>
	<rdf:li rdf:resource="http://www.aeaweb.org/articles.php?doi=10.1257/aer.104.1.27"/>
	<rdf:li rdf:resource="http://biostats.bepress.com/jhubiostat/paper259/"/>
	<rdf:li rdf:resource="http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00359"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1312.1473"/>
	<rdf:li rdf:resource="http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12044/abstract"/>
	<rdf:li rdf:resource="http://www.aeaweb.org/articles.php?doi=10.1257/jel.51.4.1120"/>
	<rdf:li rdf:resource="http://www-bcf.usc.edu/~liu32/cause.pdf"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1311.4175"/>
	<rdf:li rdf:resource="http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00374"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1309.1007"/>
	<rdf:li rdf:resource="http://economics.mit.edu/files/6988"/>
	<rdf:li rdf:resource="http://www.tandfonline.com/doi/abs/10.1080/01621459.2013.763726#.UdRPihbPUlM"/>
	<rdf:li rdf:resource="http://noahpinionblog.blogspot.com/2013/05/what-can-you-do-with-dsge-model.html"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1305.5882"/>
	<rdf:li rdf:resource="http://users.cecs.anu.edu.au/~williams/papers/P85.pdf"/>
	<rdf:li rdf:resource="http://noahpinionblog.blogspot.com/2013/03/the-swamp-of-dsge-despair.html"/>
	<rdf:li rdf:resource="http://www.cambridge.org/us/knowledge/isbn/item6852611/?site_locale=en_US"/>
	<rdf:li rdf:resource="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.bj/1358531747"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1212.5796"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1212.0463"/>
	<rdf:li rdf:resource="http://press.princeton.edu/titles/9891.html"/>
	<rdf:li rdf:resource="http://books.nips.cc/papers/files/nips23/NIPS2010_0731.pdf"/>
	<rdf:li rdf:resource="http://faculty-web.at.northwestern.edu/economics/gordon/GRU_Combined_090909.pdf"/>
	<rdf:li rdf:resource="http://riscd2.eco.ub.es/~josepgon/documents/Felipe_Fisher.pdf"/>
	<rdf:li rdf:resource="http://www.cambridge.org/us/knowledge/isbn/item5759368/?site_locale=en_US"/>
	<rdf:li rdf:resource="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1338515139"/>
	<rdf:li rdf:resource="http://www.aeaweb.org/articles.php?doi=10.1257/jep.26.2.189"/>
	<rdf:li rdf:resource="http://www.numdam.org/item?id=AIHPB_1995__31_2_393_0"/>
	<rdf:li rdf:resource="http://arxiv.org/abs/1202.4294"/>
      </rdf:Seq>
    </items>
  </channel><item rdf:about="https://arxiv.org/abs/2210.16224">
    <title>[2210.16224] Empirical Macroeconomics and DSGE Modeling in Statistical Perspective</title>
    <dc:date>2022-10-31T04:16:14+00:00</dc:date>
    <link>https://arxiv.org/abs/2210.16224</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Dynamic stochastic general equilibrium (DSGE) models have been an ubiquitous, and controversial, part of macroeconomics for decades. In this paper, we approach DSGEs purely as statstical models. We do this by applying two common model validation checks to the canonical Smets and Wouters 2007 DSGE: (1) we simulate the model and see how well it can be estimated from its own simulation output, and (2) we see how well it can seem to fit nonsense data. We find that (1) even with centuries' worth of data, the model remains poorly estimated, and (2) when we swap series at random, so that (e.g.) what the model gets as the inflation rate is really hours worked, what it gets as hours worked is really investment, etc., the fit is often only slightly impaired, and in a large percentage of cases actually improves (even out of sample). Taken together, these findings cast serious doubt on the meaningfulness of parameter estimates for this DSGE, and on whether this specification represents anything structural about the economy. Constructively, our approaches can be used for model validation by anyone working with macroeconomic time series."]]></description>
<dc:subject>in_NB self-promotion macroeconomics time_series re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:66bbc9584b8d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-promotion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2206.04902">
    <title>[2206.04902] Forecasting macroeconomic data with Bayesian VARs: Sparse or dense? It depends!</title>
    <dc:date>2022-06-13T17:40:28+00:00</dc:date>
    <link>https://arxiv.org/abs/2206.04902</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Vectorautogressions (VARs) are widely applied when it comes to modeling and forecasting macroeconomic variables. In high dimensions, however, they are prone to overfitting. Bayesian methods, more concretely shrinking priors, have shown to be successful in improving prediction performance. In the present paper we introduce the recently developed R2-induced Dirichlet-decomposition prior to the VAR framework and compare it to refinements of well-known priors in the VAR literature. We demonstrate the virtues of the proposed prior in an extensive simulation study and in an empirical application forecasting data of the US economy. Further, we shed more light on the ongoing Illusion of Sparsity debate. We find that forecasting performances under sparse/dense priors vary across evaluated economic variables and across time frames; dynamic model averaging, however, can combine the merits of both worlds. All priors are implemented using the reduced-form VAR and all models feature stochastic volatility in the variance-covariance matrix."]]></description>
<dc:subject>to:NB time_series prediction macroeconomics re:your_favorite_dsge_sucks ensemble_methods</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b6484bcc0127/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2202.06921">
    <title>[2202.06921] Simple Models and Biased Forecasts</title>
    <dc:date>2022-03-07T20:52:59+00:00</dc:date>
    <link>https://arxiv.org/abs/2202.06921</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper proposes a general framework in which agents are constrained to use simple time-series models to forecast economic variables and characterizes the resulting bias in the agents' forecasts. It considers agents who can only entertain state-space models with no more than d states, where d measures the agents' cognitive abilities. Agents' models are otherwise unrestricted a priori and disciplined endogenously by maximizing the fit to the true process. When the true process does not have a d-state representation, agents end up with misspecified models and biased forecasts. If the true process satisfies an ergodicity assumption, the bias manifests itself as persistence bias: a tendency to attend to the most persistent observables at the expense of less persistent ones. The bias causes agents' foreword-looking decisions to mimic the dynamics of backward-looking, persistent variables in the economy. It also dampens the response of agents' actions to shocks and leads to additional co-movements between various choices. The paper then proceeds to study the implications of the theory in the context of three calibrated workhorse macro models: the new-Keynesian, real business cycle, and Diamond--Mortensen--Pissarides models. In each case, constraining agents to use simple models brings the model's predictions more in line with the data, without adding any parameters other than the integer d."]]></description>
<dc:subject>to:NB misspecification prediction macroeconomics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f20fe7f16747/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/aer.20191432">
    <title>Asymmetric Attention - American Economic Association</title>
    <dc:date>2021-08-30T15:11:52+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/aer.20191432</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We document that the expectations of households, firms, and professional forecasters in standard surveys simultaneously extrapolate from recent events and underreact to new information. Existing models of expectation formation, whether behavioral or rational, cannot account for these observations. We develop a rational theory of extrapolation based on limited attention, which is consistent with this evidence. In particular, we show that limited, asymmetric attention to procyclical variables can explain the coexistence of extrapolation and underreactions. We illustrate these mechanisms in a microfounded macroeconomic model, which generates expectations consistent with the survey data, and show that asymmetric attention increases business cycle fluctuations."]]></description>
<dc:subject>to:NB prediction psychology macroeconomics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f8e69572c603/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1080/13563467.2021.1967910">
    <title>Seeing like a macroeconomist: varieties of formalisation, professional incentives and academic ideational change: New Political Economy: Vol 0, No 0</title>
    <dc:date>2021-08-19T02:13:52+00:00</dc:date>
    <link>https://doi.org/10.1080/13563467.2021.1967910</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["An emerging literature in political economy points to ‘hinges’ between academia and policy as important sites of analysis and emphasises the role of quantitative models in lending scientific legitimacy to economic ideas. This paper contributes to this literature by asking: what drives change in what is seen as authoritative macroeconomic modelling in academic settings? And how do drivers of ideational change in academia differ from drivers of ideational change in economic policy institutions? In answering these questions the paper emphasises the way in which variations in the formal structures of macroeconomic models interact with academics’ individual professional incentives. Specifically, it argues that ‘portable’ forms of modelling that do not require access to extensive resources are likely to trump ‘fixed’ and resource-intensive forms of modelling. Making this distinction helps elucidate critical junctures in the history of macroeconomic thought. Analytically, the paper relies on a framework that connects the sociology of science, the sociology of professions and the institutionalist tradition in political economy."]]></description>
<dc:subject>to:NB macroeconomics science_as_a_social_process re:your_favorite_dsge_sucks via:henry_farrell</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:dd60ee671a39/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:science_as_a_social_process"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:henry_farrell"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-081020-044812">
    <title>Estimating DSGE Models: Recent Advances and Future Challenges | Annual Review of Economics</title>
    <dc:date>2021-08-06T15:29:24+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-081020-044812</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We review the current state of the estimation of dynamic stochastic general equilibrium (DSGE) models. After introducing a general framework for dealing with DSGE models, the state-space representation, we discuss how to evaluate moments or the likelihood function implied by such a structure. We discuss, in varying degrees of detail, recent advances in the field, such as the tempered particle filter, approximated Bayesian computation, Hamiltonian Monte Carlo, variational inference, and machine learning. These methods show much promise but have not been fully explored by the DSGE community yet. We conclude by outlining three future challenges for this line of research."]]></description>
<dc:subject>to:NB to_read re:your_favorite_dsge_sucks statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:69107e7c06ff/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.rebuildingmacroeconomics.ac.uk/research-prize-complexity-macro">
    <title>Prize Complexity Macro | Rebuild Macro</title>
    <dc:date>2021-06-11T18:15:15+00:00</dc:date>
    <link>https://www.rebuildingmacroeconomics.ac.uk/research-prize-complexity-macro</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Economic Forecasting with an Agent-based Model"

--- "Cars Hommes" --- now that is is a name which I haven't heard in a long time (sadly!).]]></description>
<dc:subject>to_read macroeconomics agent-based_models re:your_favorite_dsge_sucks hommes.cars via:? in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fb0cdd4b20a6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:agent-based_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hommes.cars"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.00273">
    <title>[2007.00273] When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage</title>
    <dc:date>2021-06-10T02:09:00+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.00273</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Alternative data sets are nowadays widely used for macroeconomic nowcasting together with new Machine Learning-based tools which often are applied without having a complete picture of their theoretical nowcasting properties. Against this background, this paper proposes a theoretically-funded nowcasting methodology allowing to incorporate alternative Google Search Data (GSD) among the predictors and combining targeted preselection, Ridge regularization and Generalized Cross Validation. Breaking with most of the existing literature that focuses on asymptotic in-sample theoretical properties, we establish the theoretical out-of-sample properties of our methodology, that are supported by Monte-Carlo simulations. We apply our methodology to GSD in order to nowcast GDP growth rate of different countries during various economic periods. Our empirical findings support the idea that GSD tend to increase nowcasting accuracy, even after controlling for official variables, but that the gain differs between periods of recessions and of macroeconomic stability."]]></description>
<dc:subject>to:NB macroeconomics social_measurement econometrics re:your_favorite_dsge_sucks statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3eddc826a2fe/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2105.11182">
    <title>[2105.11182] Vector autoregression models with skewness and heavy tails</title>
    <dc:date>2021-05-26T16:02:15+00:00</dc:date>
    <link>https://arxiv.org/abs/2105.11182</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["With uncertain changes of the economic environment, macroeconomic downturns during recessions and crises can hardly be explained by a Gaussian structural shock. There is evidence that the distribution of macroeconomic variables is skewed and heavy tailed. In this paper, we contribute to the literature by extending a vector autoregression (VAR) model to account for a more realistic assumption of the multivariate distribution of the macroeconomic variables. We propose a general class of generalized hyperbolic skew Student's t distribution with stochastic volatility for the error term in the VAR model that allows us to take into account skewness and heavy tails. Tools for Bayesian inference and model selection using a Gibbs sampler are provided. In an empirical study, we present evidence of skewness and heavy tails for monthly macroeconomic variables. The analysis also gives a clear message that skewness should be taken into account for better predictions during recessions and crises."

--- What if I told you that not only are fluctuations in macroeconomic variables skewed and heavy-tailed, but that the relationships between the variables aren't linear?  Where is your Bayesian optimality god now, econometrician?
--- (The above is totally unfair to what seems like a workmanlike contribution which I should read carefully and maybe even teach next time I do time series.  But the sheer joyless slog of watching people slowly nudging econometrics towards reality, one incremental technical step at a time, feels me with weariness this morning.)]]></description>
<dc:subject>to:NB to_read time_series heavy_tails econometrics re:your_favorite_dsge_sucks statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f09d7df2e5df/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://faculty.wcas.northwestern.edu/~gep575/PriorSelectionCovid2-3.pdf">
    <title>How to Estimate a VAR after March 2020</title>
    <dc:date>2021-05-10T22:42:09+00:00</dc:date>
    <link>https://faculty.wcas.northwestern.edu/~gep575/PriorSelectionCovid2-3.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper illustrates how to handle a sequence of extreme observations—such as those
recorded during the COVID-19 pandemic—when estimating a Vector Autoregression, which
is the most popular time-series model in macroeconomics. Our results show that the ad-hoc
strategy of dropping these observations may be acceptable for the purpose of parameter estimation. However, disregarding these recent data is inappropriate for forecasting the future
evolution of the economy, because it may underestimate uncertainty."

--- Let me be somewhat unfair, and summarize this, based on skimming, as: "Have you considered 
Let's pretend this never happened' as an estimation strategy?".  And cf. d^2: "As I regularly find myself having to remind cadet risk managers with newly-minted PhDs in financial econometrics, the Great Depression did actually happen; it wasn't just a particularly inaccurate observation of the underlying 4% rate of return on equities" [http://d-squareddigest.blogspot.com/2006/09/tail-events-phrase-considered-harmful.html].  (Note the date of the post, BTW.)  If you're going to claim (pretend) that the macroeconomy is trend-stationary (or difference stationary, etc.), then years like 2020, 2009 and for that matter 1929 are all part of the stationary process, and they _should_ inform your model estimation.  If you're going to exclude those data points because they are <strike>outbreaks from the dungeon dimensions whose mere existence forces even the healthiest of econometric minds to confront the "Chaos, and Ancient Night" surrounding the Gaussian data generating process on all sides</strike> anomalous, exactly what is the process whose parameters you are estimating?]]></description>
<dc:subject>to:NB time_series coronavirus_pandemic_of_2019-- outliers econometrics via:donsker_class re:your_favorite_dsge_sucks to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0d5b2ec3b195/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coronavirus_pandemic_of_2019--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:outliers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:donsker_class"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/ectj/article-abstract/24/1/C33/5909595?redirectedFrom=fulltext">
    <title>Online estimation of DSGE models | The Econometrics Journal | Oxford Academic</title>
    <dc:date>2021-05-02T12:40:27+00:00</dc:date>
    <link>https://academic.oup.com/ectj/article-abstract/24/1/C33/5909595?redirectedFrom=fulltext</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper illustrates the usefulness of sequential Monte Carlo (SMC) methods in approximating dynamic stochastic general equilibrium (DSGE) model posterior distributions. We show how the tempering schedule can be chosen adaptively, document the accuracy and runtime benefits of generalized data tempering for ‘online’ estimation (that is, re-estimating a model as new data become available), and provide examples of multimodal posteriors that are well captured by SMC methods. We then use the online estimation of the DSGE model to compute pseudo-out-of-sample density forecasts and study the sensitivity of the predictive performance to changes in the prior distribution. We find that making priors less informative (compared with the benchmark priors used in the literature) by increasing the prior variance does not lead to a deterioration of forecast accuracy."]]></description>
<dc:subject>to:NB to_read dsges particle_filters re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:348d636f156a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dsges"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:particle_filters"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1705.00395">
    <title>[1705.00395] Inverse Moment Methods for Sufficient Forecasting using High-Dimensional Predictors</title>
    <dc:date>2021-04-22T15:22:57+00:00</dc:date>
    <link>https://arxiv.org/abs/1705.00395</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider forecasting a single time series using a large number of predictors in the presence of a possible nonlinear forecast function. Assuming that the predictors affect the response through the latent factors, we propose to first conduct factor analysis and then apply sufficient dimension reduction on the estimated factors, to derive the reduced data for subsequent forecasting. Using directional regression and the inverse third-moment method in the stage of sufficient dimension reduction, the proposed methods can capture the non-monotone effect of factors on the response. We also allow a diverging number of factors and only impose general regularity conditions on the distribution of factors, avoiding the undesired time reversibility of the factors by the latter. These make the proposed methods fundamentally more applicable than the sufficient forecasting method in Fan et al. (2017). The proposed methods are demonstrated in both simulation studies and an empirical study of forecasting monthly macroeconomic data from 1959 to 2016. Also, our theory contributes to the literature of sufficient dimension reduction, as it includes an invariance result, a path to perform sufficient dimension reduction under the high-dimensional setting without assuming sparsity, and the corresponding order-determination procedure."]]></description>
<dc:subject>to:NB prediction time_series factor_analysis dimension_reduction to_read re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:595aabfa2cb8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:factor_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dimension_reduction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nber.org/papers/w23673">
    <title>Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data | NBER</title>
    <dc:date>2021-04-11T03:31:25+00:00</dc:date>
    <link>https://www.nber.org/papers/w23673</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper seeks to better understand what makes big data analysis different, what we can and cannot do with existing econometric tools, and what issues need to be dealt with in order to work with the data efficiently. As a case study, I set out to extract any business cycle information that might exist in four terabytes of weekly scanner data. The main challenge is to handle the volume, variety, and characteristics of the data within the constraints of our computing environment. Scalable and efficient algorithms are available to ease the computation burden, but they often have unknown statistical properties and are not designed for the purpose of efficient estimation or optimal inference. As well, economic data have unique characteristics that generic algorithms may not accommodate. There is a need for computationally efficient econometric methods as big data is likely here to stay."]]></description>
<dc:subject>to:NB data_mining macroeconomics ng.serena heard_the_talk to_read re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:59ca1ea7aed8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ng.serena"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heard_the_talk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://link.springer.com/article/10.1007/s11238-012-9305-8">
    <title>D-separation, forecasting, and economic science: a conjecture | SpringerLink</title>
    <dc:date>2021-04-05T14:55:39+00:00</dc:date>
    <link>https://link.springer.com/article/10.1007/s11238-012-9305-8</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The paper considers the conjecture that forecasts from preferred economic models or theories d-separate forecasts from less preferred models or theories from the Actual realization of the variable for which a scientific explanation is sought. D-separation provides a succinct notion to represent forecast dominance of one set of forecasts over another; it provides, as well, a criterion for model preference as a fundamental device for progress in economic science. We demonstrate these ideas with examples from three areas of economic modeling."]]></description>
<dc:subject>to:NB model_selection prediction causal_inference causal_discovery social_science_methodology econometrics macroeconomics re:your_favorite_dsge_sucks have_skimmed</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:80b199897417/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_discovery"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00718">
    <title>A Composite Likelihood Framework for Analyzing Singular DSGE Models | The Review of Economics and Statistics | MIT Press Journals</title>
    <dc:date>2019-01-04T03:27:21+00:00</dc:date>
    <link>https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00718</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper builds on the composite likelihood concept of Lindsay (1988) to develop a framework for parameter identification, estimation, inference, and forecasting in dynamic stochastic general equilibrium (DSGE) models allowing for stochastic singularity. The framework consists of four components. First, it provides a necessary and sufficient condition for parameter identification, where the identifying information is provided by the first- and second-order properties of nonsingular submodels. Second, it provides a procedure based on Markov Chain Monte Carlo for parameter estimation. Third, it delivers confidence sets for structural parameters and impulse responses that allow for model misspecification. Fourth, it generates forecasts for all the observed endogenous variables, irrespective of the number of shocks in the model. The framework encompasses the conventional likelihood analysis as a special case when the model is nonsingular. It enables the researcher to start with a basic model and then gradually incorporate more shocks and other features, meanwhile confronting all the models with the data to assess their implications. The methodology is illustrated using both small- and medium-scale DSGE models. These models have numbers of shocks ranging between 1 and 7."]]></description>
<dc:subject>state-space_models economics time_series macroeconomics statistics likelihood re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3f15d77b79b3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:state-space_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:likelihood"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jep.32.3.141">
    <title>Evolution of Modern Business Cycle Models: Accounting for the Great Recession</title>
    <dc:date>2018-08-02T00:33:23+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jep.32.3.141</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Modern business cycle theory focuses on the study of dynamic stochastic general equilibrium (DSGE) models that generate aggregate fluctuations similar to those experienced by actual economies. We discuss how these modern business cycle models have evolved across three generations, from their roots in the early real business cycle models of the late 1970s through the turmoil of the Great Recession four decades later. The first generation models were real (that is, without a monetary sector) business cycle models that primarily explored whether a small number of shocks, often one or two, could generate fluctuations similar to those observed in aggregate variables such as output, consumption, investment, and hours. These basic models disciplined their key parameters with micro evidence and were remarkably successful in matching these aggregate variables. A second generation of these models incorporated frictions such as sticky prices and wages; these models were primarily developed to be used in central banks for short-term forecasting purposes and for performing counterfactual policy experiments. A third generation of business cycle models incorporate the rich heterogeneity of patterns from the micro data. A defining characteristic of these models is not the heterogeneity among model agents they accommodate nor the micro-level evidence they rely on (although both are common), but rather the insistence that any new parameters or feature included be explicitly disciplined by direct evidence. We show how two versions of this latest generation of modern business cycle models, which are real business cycle models with frictions in labor and financial markets, can account, respectively, for the aggregate and the cross-regional fluctuations observed in the United States during the Great Recession."]]></description>
<dc:subject>to:NB macroeconomics re:your_favorite_dsge_sucks financial_crisis_of_2007-- economics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:73fb213d670a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_crisis_of_2007--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jep.32.3.113">
    <title>On DSGE Models</title>
    <dc:date>2018-08-02T00:32:16+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jep.32.3.113</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The outcome of any important macroeconomic policy change is the net effect of forces operating on different parts of the economy. A central challenge facing policymakers is how to assess the relative strength of those forces. Economists have a range of tools that can be used to make such assessments. Dynamic stochastic general equilibrium (DSGE) models are the leading tool for making such assessments in an open and transparent manner. We review the state of mainstream DSGE models before the financial crisis and the Great Recession. We then describe how DSGE models are estimated and evaluated. We address the question of why DSGE modelers—like most other economists and policymakers—failed to predict the financial crisis and the Great Recession, and how DSGE modelers responded to the financial crisis and its aftermath. We discuss how current DSGE models are actually used by policymakers. We then provide a brief response to some criticisms of DSGE models, with special emphasis on criticism by Joseph Stiglitz, and offer some concluding remarks."]]></description>
<dc:subject>to:NB macroeconomics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0da532902d82/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jep.32.3.59">
    <title>Identification in Macroeconomics</title>
    <dc:date>2018-08-02T00:31:09+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jep.32.3.59</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper discusses empirical approaches macroeconomists use to answer questions like: What does monetary policy do? How large are the effects of fiscal stimulus? What caused the Great Recession? Why do some countries grow faster than others? Identification of causal effects plays two roles in this process. In certain cases, progress can be made using the direct approach of identifying plausibly exogenous variation in a policy and using this variation to assess the effect of the policy. However, external validity concerns limit what can be learned in this way. Carefully identified causal effects estimates can also be used as moments in a structural moment matching exercise. We use the term "identified moments" as a short-hand for "estimates of responses to identified structural shocks," or what applied microeconomists would call "causal effects." We argue that such identified moments are often powerful diagnostic tools for distinguishing between important classes of models (and thereby learning about the effects of policy). To illustrate these notions we discuss the growing use of cross-sectional evidence in macroeconomics and consider what the best existing evidence is on the effects of monetary policy."]]></description>
<dc:subject>to:NB causal_inference macroeconomics economics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6888f4f68114/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2684776">
    <title>The Non-Existence of Representative Agents by Matthew O. Jackson, Leeat Yariv :: SSRN</title>
    <dc:date>2018-07-18T14:33:24+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2684776</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We characterize environments in which there exists a representative agent: an agent who inherits the structure of preferences of the population that she represents. The existence of such a representative agent imposes strong restrictions on individual utility functions -- requiring them to be linear in the allocation and additively separable in any parameter that characterizes agents' preferences (e.g., a risk aversion parameter, a discount factor, etc.). Commonly used classes of utility functions (exponentially discounted utility functions, CRRA or CARA utility functions, logarithmic functions, etc.) do not admit a representative agent."]]></description>
<dc:subject>economics macroeconomics macro_from_micro aggregation jackson.matthew_o. re:your_favorite_dsge_sucks in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:92ae0b7d8444/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:aggregation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:jackson.matthew_o."/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00870">
    <title>Learning Theory Estimates with Observations from General Stationary Stochastic Processes | Neural Computation | MIT Press Journals</title>
    <dc:date>2016-11-23T18:21:15+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/NECO_a_00870</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This letter investigates the supervised learning problem with observations drawn from certain general stationary stochastic processes. Here by general, we mean that many stationary stochastic processes can be included. We show that when the stochastic processes satisfy a generalized Bernstein-type inequality, a unified treatment on analyzing the learning schemes with various mixing processes can be conducted and a sharp oracle inequality for generic regularized empirical risk minimization schemes can be established. The obtained oracle inequality is then applied to derive convergence rates for several learning schemes such as empirical risk minimization (ERM), least squares support vector machines (LS-SVMs) using given generic kernels, and SVMs using gaussian kernels for both least squares and quantile regression. It turns out that for independent and identically distributed (i.i.d.) processes, our learning rates for ERM recover the optimal rates. For non-i.i.d. processes, including geometrically -mixing Markov processes, geometrically -mixing processes with restricted decay, -mixing processes, and (time-reversed) geometrically -mixing processes, our learning rates for SVMs with gaussian kernels match, up to some arbitrarily small extra term in the exponent, the optimal rates. For the remaining cases, our rates are at least close to the optimal rates. As a by-product, the assumed generalized Bernstein-type inequality also provides an interpretation of the so-called effective number of observations for various mixing processes."]]></description>
<dc:subject>stochastic_processes learning_theory dependence_measures mixing ergodic_theory statistics re:XV_for_mixing re:your_favorite_dsge_sucks in_NB to_teach:childs_garden_of_statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5c929691bae6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dependence_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://krugman.blogs.nytimes.com/2016/08/12/the-state-of-macro-is-sad-wonkish/">
    <title>The State of Macro Is Sad (Wonkish) - The New York Times</title>
    <dc:date>2016-08-15T15:17:11+00:00</dc:date>
    <link>http://krugman.blogs.nytimes.com/2016/08/12/the-state-of-macro-is-sad-wonkish/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>have_read re:your_favorite_dsge_sucks macroeconomics krugman.paul</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:394f044f2903/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:krugman.paul"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://piie.com/system/files/documents/pb16-11.pdf">
    <title>Do DSGE Models Have a Future?</title>
    <dc:date>2016-08-11T15:31:24+00:00</dc:date>
    <link>https://piie.com/system/files/documents/pb16-11.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB to_read re:your_favorite_dsge_sucks economics macroeconomics blanchard.olivier via:jbdelong color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5126eeee81ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:blanchard.olivier"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jbdelong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/10612.html">
    <title>Herbst, E.P. and Schorfheide, F.: Bayesian Estimation of DSGE Models (eBook and Hardcover).</title>
    <dc:date>2016-01-04T18:09:41+00:00</dc:date>
    <link>http://press.princeton.edu/titles/10612.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations."

]]></description>
<dc:subject>to:NB books:noted econometrics macroeconomics time_series estimation statistics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:faa593baef93/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://public.econ.duke.edu/~kdh9/Source%20Materials/Research/Reductionism-Economics14May2015.pdf">
    <title>Reductionism in Economics: Intentionality and Eschatological Justification in the Microfoundations of Macroeconomics</title>
    <dc:date>2015-08-03T04:31:42+00:00</dc:date>
    <link>http://public.econ.duke.edu/~kdh9/Source%20Materials/Research/Reductionism-Economics14May2015.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Macroeconomists overwhelmingly believe that macroeconomics requires microfoundations, typically understood as a strong eliminativist reductionism. Microfoundations aims to recover intentionality. In the face of technical and data constraints macroeconomists typically employ a representative-agent model, in which a single agent solves microeconomic optimization problem for the whole economy, and take it to be microfoundationally adequate. The characteristic argument for the representative-agent model holds that the possibility of the sequential elaboration of the model to cover any number of individual agents justifies treating the policy conclusions of the single-agent model as practically relevant. This eschatological justification is examined and rejected."]]></description>
<dc:subject>have_read economics reductionism macroeconomics social_science_methodology philosophy_of_science re:your_favorite_dsge_sucks via:jbdelong in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:844091280b33/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reductionism"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:philosophy_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jbdelong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://rjwaldmann.blogspot.com/2012/03/modern-macroeconomic-methodology-modern.html">
    <title>Robert's Stochastic thoughts: Modern Macroeconomic Methodology</title>
    <dc:date>2015-05-07T22:51:19+00:00</dc:date>
    <link>http://rjwaldmann.blogspot.com/2012/03/modern-macroeconomic-methodology-modern.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>social_science_methodology economics macroeconomics waldmann.robert re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:561c14da7ac2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:waldmann.robert"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.3192">
    <title>[1410.3192] Learning without Concentration for General Loss Functions</title>
    <dc:date>2015-01-20T02:19:39+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.3192</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study prediction and estimation problems using empirical risk minimization, relative to a general convex loss function. We obtain sharp error rates even when concentration is false or is very restricted, for example, in heavy-tailed scenarios. Our results show that the error rate depends on two parameters: one captures the intrinsic complexity of the class, and essentially leads to the error rate in a noise-free (or realizable) problem; the other measures interactions between class members the target and the loss, and is dominant when the problem is far from realizable. We also explain how one may deal with outliers by choosing the loss in a way that is calibrated to the intrinsic complexity of the class and to the noise-level of the problem (the latter is measured by the distance between the target and the class)."]]></description>
<dc:subject>to:NB learning_theory heavy_tails statistics to_read re:your_favorite_dsge_sucks to_teach:childs_garden_of_statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:186cf300e45d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.voxeu.org/article/how-good-are-out-sample-forecasting-tests">
    <title>How good are out-of-sample forecasting tests? | VOX, CEPR’s Policy Portal</title>
    <dc:date>2015-01-15T22:26:09+00:00</dc:date>
    <link>http://www.voxeu.org/article/how-good-are-out-sample-forecasting-tests</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Out-of-sample forecasting tests are increasingly used to establish the quality of macroeconomic models. This column discusses recent research that assesses what these tests can establish with confidence about macroeconomic models’ specification and forecasting ability. Using a Monte Carlo experiment on a widely used macroeconomic model, the authors find that out-of-sample forecasting tests have weak power against misspecification and forecasting performance. However, an in-sample indirect inference test can be used to establish reliably both the model’s specification quality and its forecasting capacity."

--- Except they don't run tests with _mis-specification_, they run tests with _changes in the parameters_.  I am not at all surprised that the forecasts of the Smets-Wooters DSGE are fairly insensitive to the parameters.  But to see the power of out-of-sample forecasting to detect mis-specification, you'd need to do runs where the data-generating process wasn't a Smets-Wooters DSGE with any parameter setting at all.]]></description>
<dc:subject>to:NB track_down_references economics macroeconomics econometrics prediction hypothesis_testing re:your_favorite_dsge_sucks have_read via:djm1107</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8d636cff68ba/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hypothesis_testing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:djm1107"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cims.nyu.edu/~vitaly/pub/fts.pdf">
    <title>Forecasting Nonstationary Time Series: From Theory to Algorithms</title>
    <dc:date>2014-12-17T18:09:44+00:00</dc:date>
    <link>http://www.cims.nyu.edu/~vitaly/pub/fts.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Generalization bounds for time series prediction and other non-i.i.d. learning sce- narios that can be found in the machine learning and statistics literature assume that observations come from a (strictly) stationary distribution. The first bounds for completely non-stationary setting were proved in [6]. In this work we present an extension of these results and derive novel algorithms for forecasting non- stationary time series. Our experimental results show that our algorithms sig- nificantly outperform standard autoregressive models commonly used in practice."

--- Assumes mixing but not stationary.]]></description>
<dc:subject>to:NB mixing learning_theory re:your_favorite_dsge_sucks re:XV_for_mixing time_series have_read to_teach:childs_garden_of_statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5aee25e7a5fb/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.voxeu.org/article/when-economic-models-are-unable-fit-data">
    <title>When economic models are unable to fit the data | VOX, CEPR’s Policy Portal</title>
    <dc:date>2014-11-24T04:01:01+00:00</dc:date>
    <link>http://www.voxeu.org/article/when-economic-models-are-unable-fit-data</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Shorter: if your model claims to include all the relevant variables and throwing more covariates into your regression improves your fit, you have a problem.  (But I would be shocked if they are really doing an adequate job of accounting for specification-search and model-selection issues here.)]]></description>
<dc:subject>track_down_references economics model_selection misspecification goodness-of-fit econometrics statistics baby_steps to:blog re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:941ebb031700/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:goodness-of-fit"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:baby_steps"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciencedirect.com/science/article/pii/S0169207097000307">
    <title>Forecasting economic time series using flexible versus fixed specification and linear versus nonlinear econometric models</title>
    <dc:date>2014-09-24T22:05:17+00:00</dc:date>
    <link>http://www.sciencedirect.com/science/article/pii/S0169207097000307</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Nine macroeconomic variables are forecast in a real-time scenario using a variety of flexible specification, fixed specification, linear, and nonlinear econometric models. All models are allowed to evolve through time, and our analysis focuses on model selection and performance. In the context of real-time forecasts, flexible specification models (including linear autoregressive models with exogenous variables and nonlinear artificial neural networks) appear to offer a useful and viable alternative to less flexible fixed specification linear models for a subset of the economic variables which we examine, particularly at forecast horizons greater than 1-step ahead. We speculate that one reason for this result is that the economy is evolving (rather slowly) over time. This feature cannot easily be captured by fixed specification linear models, however, and manifests itself in the form of evolving coefficient estimates. We also provide additional evidence supporting the claim that models which ‘win’ based on one model selection criterion (say a squared error measure) do not necessarily win when an alternative selection criterion is used (say a confusion rate measure), thus highlighting the importance of the particular cost function which is used by forecasters and ‘end-users’ to evaluate their models. A wide variety of different model selection criteria and statistical tests are used to illustrate our findings."]]></description>
<dc:subject>to:NB economics macroeconomics prediction white.halbert to_read re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e879a5ffa04e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:white.halbert"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://equitablegrowth.org/2014/07/20/state-macroeconomics-good-monday-focus-july-21-2014/">
    <title>The State of Macroeconomics? Not Good...: Monday Focus for July 21, 2014 | Washington Center for Equitable Growth</title>
    <dc:date>2014-07-22T01:07:15+00:00</dc:date>
    <link>http://equitablegrowth.org/2014/07/20/state-macroeconomics-good-monday-focus-july-21-2014/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>macroeconomics social_science_methodology economics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0eea229477af/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://angrybearblog.com/2014/07/25621.html">
    <title>Angry Bear » Comment on Del Negro, Giannoni &amp; Schorfheide (2014)</title>
    <dc:date>2014-07-15T16:59:30+00:00</dc:date>
    <link>http://angrybearblog.com/2014/07/25621.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["My objection is that, since in practice all deviations between micro founded models and an ad hoc aggregate models are bugs not features, what possible use could there ever be in micro founding models."]]></description>
<dc:subject>macroeconomics financial_crisis_of_2007-- economics dsges re:your_favorite_dsge_sucks social_science_methodology have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:eaa91198fa16/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_crisis_of_2007--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dsges"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.2462">
    <title>[1406.2462] Empirical risk minimization for heavy-tailed losses</title>
    <dc:date>2014-07-12T00:31:20+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.2462</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The purpose of this paper is to discuss empirical risk minimization when the losses are not necessarily bounded and may have a distribution with heavy tails. In such situations usual empirical averages may fail to provide reliable estimates and empirical risk minimization may provide large excess risk. However, some robust mean estimators proposed in the literature may be used to replace empirical means. In this paper we investigate empirical risk minimization based on a robust estimate proposed by Catoni. We develop performance bounds based on chaining arguments tailored to Catoni's mean estimator."]]></description>
<dc:subject>learning_theory heavy_tails statistics re:your_favorite_dsge_sucks in_NB to_teach:childs_garden_of_statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:524f7b4602c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:heavy_tails"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1406.1037">
    <title>[1406.1037] Bootstrapping High Dimensional Time Series</title>
    <dc:date>2014-07-12T00:26:47+00:00</dc:date>
    <link>http://arxiv.org/abs/1406.1037</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We focus on the problem of conducting inference for high dimensional weakly dependent time series. Our results are motivated by the applications in modern high dimensional inference including (1) constructing uniform confidence band for high dimensional mean vector and (2) specification testing on the second order property of high dimensional time series such as white noise testing and testing for bandedness of covariance matrix. In theory, we derive a Gaussian approximation result for the maximum of a sum of weakly dependent vectors by adapting Stein's method, where the dimension of the vectors is allowed to be exponentially larger than the sample size. Our result reveals an interesting phenomenon arising from the interplay between the dependence and dimensionality: the more dependent of the data vectors, the slower diverging rate of the dimension is allowed for obtaining valid statistical inference. Building on the Gaussian approximation result, we propose a blockwise multiplier (wild) bootstrap that is able to capture the dependence amongst and within the data vectors and thus provides high-quality distributional approximation to the distribution of the maximum of vector sum in the high dimensional context."]]></description>
<dc:subject>bootstrap time_series high-dimensional_statistics statistics re:your_favorite_dsge_sucks in_NB have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:9fb90f363cf2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.sciencedirect.com/science/article/pii/S0304407614000761">
    <title>Unpredictability in economic analysis, econometric modeling and forecasting</title>
    <dc:date>2014-06-27T20:32:21+00:00</dc:date>
    <link>http://www.sciencedirect.com/science/article/pii/S0304407614000761</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Unpredictability arises from intrinsic stochastic variation, unexpected instances of outliers, and unanticipated extrinsic shifts of distributions. We analyze their properties, relationships, and different effects on the three arenas in the title, which suggests considering three associated information sets. The implications of unanticipated shifts for forecasting, economic analyses of efficient markets, conditional expectations, and inter-temporal derivations are described. The potential success of general-to-specific model selection in tackling location shifts by impulse-indicator saturation is contrasted with the major difficulties confronting forecasting."]]></description>
<dc:subject>to:NB prediction non-stationarity econometrics statistics re:your_favorite_dsge_sucks re:growing_ensemble_project to_read via:djm1107</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7c82985eb9c0/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-stationarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:growing_ensemble_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:djm1107"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/0707.0322">
    <title>[0707.0322] Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise</title>
    <dc:date>2014-03-12T20:37:56+00:00</dc:date>
    <link>http://arxiv.org/abs/0707.0322</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the problem of forecasting the next (observable) state of an unknown ergodic dynamical system from a noisy observation of the present state. Our main result shows, for example, that support vector machines (SVMs) using Gaussian RBF kernels can learn the best forecaster from a sequence of noisy observations if (a) the unknown observational noise process is bounded and has a summable α-mixing rate and (b) the unknown ergodic dynamical system is defined by a Lipschitz continuous function on some compact subset of ℝd and has a summable decay of correlations for Lipschitz continuous functions. In order to prove this result we first establish a general consistency result for SVMs and all stochastic processes that satisfy a mixing notion that is substantially weaker than α-mixing."]]></description>
<dc:subject>dynamical_systems mixing ergodic_theory nonparametrics statistics prediction support-vector_machines steinwart.ingo time_series statistical_inference_for_stochastic_processes re:your_favorite_dsge_sucks re:XV_for_mixing to_read in_NB entableted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a11e92b7fc51/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ergodic_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:support-vector_machines"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:steinwart.ingo"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:entableted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1403.0740">
    <title>[1403.0740] On the Information-theoretic Limits of Graphical Model Selection for Gaussian Time Series</title>
    <dc:date>2014-03-08T22:09:56+00:00</dc:date>
    <link>http://arxiv.org/abs/1403.0740</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We consider the problem of inferring the conditional independence graph (CIG) of a multivariate stationary dicrete-time Gaussian random process based on a finite length observation. Using information-theoretic methods, we derive a lower bound on the error probability of any learning scheme for the underlying process CIG. This bound, in turn, yields a minimum required sample-size which is necessary for any algorithm regardless of its computational complexity, to reliably select the true underlying CIG. Furthermore, by analysis of a simple selection scheme, we show that the information-theoretic limits can be achieved for a subclass of processes having sparse CIG. We do not assume a parametric model for the observed process, but require it to have a sufficiently smooth spectral density matrix (SDM)."]]></description>
<dc:subject>to:NB graphical_models conditional_independence information_theory learning_theory re:your_favorite_dsge_sucks time_series statistics to_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:714040ff7818/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:conditional_independence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:information_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/aer.104.2.379">
    <title>AER (104,2) p. 379 - A Macroeconomic Model with a Financial Sector</title>
    <dc:date>2014-02-03T19:54:26+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/aer.104.2.379</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article studies the full equilibrium dynamics of an economy with financial frictions. Due to highly nonlinear amplification effects, the economy is prone to instability and occasionally enters volatile crisis episodes. Endogenous risk, driven by asset illiquidity, persists in crisis even for very low levels of exogenous risk. This phenomenon, which we call the volatility paradox, resolves the Kocherlakota (2000) critique. Endogenous leverage determines the distance to crisis. Securitization and derivatives contracts that improve risk sharing may lead to higher leverage and more frequent crises."]]></description>
<dc:subject>to:NB economics macroeconomics financial_crisis_of_2007-- re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1d1ccc0b47b7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_crisis_of_2007--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://noahpinionblog.blogspot.com/2014/01/the-equation-at-core-of-modern-macro.html">
    <title>Noahpinion: The equation at the core of modern macro</title>
    <dc:date>2014-01-15T20:02:29+00:00</dc:date>
    <link>http://noahpinionblog.blogspot.com/2014/01/the-equation-at-core-of-modern-macro.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[In defense of the Euler equation (*): why assume that the Fed funds rate / risk-free loan interest rate is the rate of time preference, or even closely correlated with the rate of time preference?  Surely the r.o.t.p. is at most one component of even the risk-free interest rate.  (I believe I am stealing this argument from J. W. Mason.)  --- The point about checking intermediate parts of the model is however entirely sound (and not handled just by doing a generalized-method-of-moments estimate for each equation).]]></description>
<dc:subject>economics macroeconomics re:your_favorite_dsge_sucks social_science_methodology model_checking</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2a0f0469051b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.4825">
    <title>[1305.4825] Learning subgaussian classes : Upper and minimax bounds</title>
    <dc:date>2014-01-13T19:04:17+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.4825</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We obtain sharp oracle inequalities for the empirical risk minimization procedure in the regression model under the assumption that the target Y and the model $\cF$ are subgaussian. The bound we obtain is sharp in the minimax sense if $\cF$ is convex. Moreover, under mild assumptions on $\cF$, the error rate of ERM remains optimal even if the procedure is allowed to perform with constant probability. A part of our analysis is a new proof of minimax results for the gaussian regression model."]]></description>
<dc:subject>regression learning_theory to_read re:your_favorite_dsge_sucks in_NB to_teach:childs_garden_of_statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f21e15eadf8b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1401.0304">
    <title>[1401.0304] Learning without Concentration</title>
    <dc:date>2014-01-04T19:44:55+00:00</dc:date>
    <link>http://arxiv.org/abs/1401.0304</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We obtain sharp bounds on the performance of Empirical Risk Minimization performed in a convex class and with respect to the squared loss, without any boundedness assumptions on class members or on the target. Rather than resorting to a concentration-based argument, the method relies on a `small-ball' assumption and thus holds for heavy-tailed sampling and heavy-tailed targets. Moreover, the resulting estimates scale correctly with the `noise'. When applied to the classical, bounded scenario, the method always improves the known estimates."]]></description>
<dc:subject>learning_theory re:your_favorite_dsge_sucks re:XV_for_mixing have_read in_NB to_teach:childs_garden_of_statistical_learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4be5cac1cead/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/aer.104.1.27">
    <title>AER (104,1) p. 27 - Risk Shocks</title>
    <dc:date>2014-01-03T19:53:00+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/aer.104.1.27</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We augment a standard monetary dynamic general equilibrium model to include a Bernanke-Gertler-Gilchrist financial accelerator mechanism. We fit the model to US data, allowing the volatility of cross-sectional idiosyncratic uncertainty to fluctuate over time. We refer to this measure of volatility as risk. We find that fluctuations in risk are the most important shock driving the business cycle."]]></description>
<dc:subject>to:NB macroeconomics economics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:569c448206e4/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://biostats.bepress.com/jhubiostat/paper259/">
    <title>Joint Estimation of Multiple Graphical Models from High Dimensional Time Series</title>
    <dc:date>2014-01-02T18:37:34+00:00</dc:date>
    <link>http://biostats.bepress.com/jhubiostat/paper259/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this manuscript the problem of jointly estimating multiple graphical models in high dimensions is considered. It is assumed that the data are collected from n subjects, each of which consists of m non-independent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of the closeness between subjects. A kernel based method for jointly estimating all graphical models is proposed. Theoretically, under a double asymptotic framework, where both (m,n) and the dimension d can increase, the explicit rate of convergence in parameter estimation is provided, thus characterizing the strength one can borrow across different individuals and impact of data dependence on parameter estimation. Empirically, experiments on both synthetic and real resting state functional magnetic resonance imaging (rs-fMRI) data illustrate the effectiveness of the proposed method."]]></description>
<dc:subject>to:NB to_read graphical_models time_series high-dimensional_statistics kernel_estimators liu.han re:your_favorite_dsge_sucks fmri</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:afc4a7770ffa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kernel_estimators"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:liu.han"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fmri"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00359">
    <title>Dynamic Hierarchical Factor Models</title>
    <dc:date>2014-01-02T00:48:20+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00359</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper uses multilevel factor models to characterize within- and between-block variations as well as idiosyncratic noise in large dynamic panels. Block-level shocks are distinguished from genuinely common shocks, and the estimated block-level factors are easy to interpret. The framework achieves dimension reduction and yet explicitly allows for heterogeneity between blocks. The model is estimated using an MCMC algorithm that takes into account the hierarchical structure of the factors. The importance of block-level variations is illustrated in a four-level model estimated on a panel of 445 series related to different categories of real activity in the United States."]]></description>
<dc:subject>time_series inference_to_latent_objects economics macroeconomics factor_analysis hierarchical_statistical_models statistics re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3c88c67e06a7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inference_to_latent_objects"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:factor_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hierarchical_statistical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1312.1473">
    <title>[1312.1473] Oracle Properties and Finite Sample Inference of the Adaptive Lasso for Time Series Regression Models</title>
    <dc:date>2013-12-26T00:33:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1312.1473</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We derive new theoretical results on the properties of the adaptive least absolute shrinkage and selection operator (adaptive lasso) for time series regression models. In particular, we investigate the question of how to conduct finite sample inference on the parameters given an adaptive lasso model for some fixed value of the shrinkage parameter. Central in this study is the test of the hypothesis that a given adaptive lasso parameter equals zero, which therefore tests for a false positive. To this end we construct a simple testing procedure and show, theoretically and empirically through extensive Monte Carlo simulations, that the adaptive lasso combines efficient parameter estimation, variable selection, and valid finite sample inference in one step. Moreover, we analytically derive a bias correction factor that is able to significantly improve the empirical coverage of the test on the active variables. Finally, we apply the introduced testing procedure to investigate the relation between the short rate dynamics and the economy, thereby providing a statistical foundation (from a model choice perspective) to the classic Taylor rule monetary policy model."]]></description>
<dc:subject>lasso time_series variable_selection statistics re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7fcc3eb7d15b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lasso"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:variable_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12044/abstract">
    <title>NON-PARAMETRIC ESTIMATION UNDER STRONG DEPENDENCE - Zhao - 2013 - Journal of Time Series Analysis - Wiley Online Library</title>
    <dc:date>2013-12-23T22:06:58+00:00</dc:date>
    <link>http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12044/abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study non-parametric regression function estimation for models with strong dependence. Compared with short-range dependent models, long-range dependent models often result in slower convergence rates. We propose a simple differencing-sequence based non-parametric estimator that achieves the same convergence rate as if the data were independent. Simulation studies show that the proposed method has good finite sample performance."

- The trick here is to only estimate the independence on an observed _and_ IID covariate, i.e., the model is Y(t) = m(X(t)) + g(t) + \epsilon_t, where X(t) is the IID covariate, g(t) is an (unknown) time-trend, and \epsilon_t is the long-range-dependent innovation sequence.  Differencing, Y(t) - Y(t-1) = m(X(t)) - m(X(t-1)) + stuff which averages rapidly, so one can learn the m() function up to an over-all constant rapidly.  Worth mentioning in the ADA notes, in the sense of "don't solve hard problems you don't have to", but not a fundamental advance.]]></description>
<dc:subject>to:NB time_series statistical_inference_for_stochastic_processes statistics re:your_favorite_dsge_sucks have_read nonparametrics to_teach:undergrad-ADA kernel_smoothing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d894e0711352/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kernel_smoothing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/jel.51.4.1120">
    <title>JEL (51,4) p. 1120 - Facts and Challenges from the Great Recession for Forecasting and Macroeconomic Modeling</title>
    <dc:date>2013-12-12T16:41:25+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/jel.51.4.1120</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper provides a survey of business cycle facts, updated to take account of recent data. Emphasis is given to the Great Recession, which was unlike most other postwar recessions in the United States in being driven by deleveraging and financial market factors. We document how recessions with financial market origins are different from those driven by supply or monetary policy shocks. This helps explain why economic models and predictors that work well at some times do poorly at other times. We discuss challenges for forecasters and empirical researchers in light of the updated business cycle facts."]]></description>
<dc:subject>to:NB economics macroeconomics financial_crisis_of_2007-- time_series re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:27b879922b58/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_crisis_of_2007--"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www-bcf.usc.edu/~liu32/cause.pdf">
    <title>Causality Analysis in Large Scale Time Series Data</title>
    <dc:date>2013-11-22T19:16:15+00:00</dc:date>
    <link>http://www-bcf.usc.edu/~liu32/cause.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>track_down_references time_series causal_inference graphical_models re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:163787d05ecc/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:track_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1311.4175">
    <title>[1311.4175] Estimation in High-dimensional Vector Autoregressive Models</title>
    <dc:date>2013-11-21T17:41:27+00:00</dc:date>
    <link>http://arxiv.org/abs/1311.4175</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Vector Autoregression (VAR) is a widely used method for learning complex interrelationship among the components of multiple time series. Over the years it has gained popularity in the fields of control theory, statistics, economics, finance, genetics and neuroscience. We consider the problem of estimating stable VAR models in a high-dimensional setting, where both the number of time series and the VAR order are allowed to grow with sample size. In addition to the ``curse of dimensionality" introduced by a quadratically growing dimension of the parameter space, VAR estimation poses considerable challenges due to the temporal and cross-sectional dependence in the data. Under a sparsity assumption on the model transition matrices, we establish estimation and prediction consistency of ℓ1-penalized least squares and likelihood based methods. Exploiting spectral properties of stationary VAR processes, we develop novel theoretical techniques that provide deeper insight into the effect of dependence on the convergence rates of the estimates. We study the impact of error correlations on the estimation problem and develop fast, parallelizable algorithms for penalized likelihood based VAR estimates."]]></description>
<dc:subject>time_series sparsity statistics re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:00527dc690c7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sparsity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00374">
    <title>Expectations and Economic Fluctuations: An Analysis Using Survey Data</title>
    <dc:date>2013-10-01T16:33:05+00:00</dc:date>
    <link>http://www.mitpressjournals.org/doi/abs/10.1162/REST_a_00374</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Using survey-based measures of future U.S. economic activity from the Livingston Survey and the Survey of Professional Forecasters, we study how changes in expectations and their interaction with monetary policy contribute to fluctuations in macroeconomic aggregates. We find that changes in expected future economic activity are a quantitatively important driver of economic fluctuations: a perception that good times are ahead typically leads to a significant rise in current measures of economic activity and inflation. We also find that the short-term interest rate rises in response to expectations of good times as monetary policy tightens."]]></description>
<dc:subject>to:NB economics macroeconomics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:42dd490c9a95/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1309.1007">
    <title>[1309.1007] Concentration in unbounded metric spaces and algorithmic stability</title>
    <dc:date>2013-09-05T12:48:11+00:00</dc:date>
    <link>http://arxiv.org/abs/1309.1007</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We prove an extension of McDiarmid's inequality for metric spaces with unbounded diameter. To this end, we introduce the notion of the {\em subgaussian diameter}, which is a distribution-dependent refinement of the metric diameter. Our technique provides an alternative approach to that of Kutin and Niyogi's method of weakly difference-bounded functions, and yields nontrivial, dimension-free results in some interesting cases where the former does not. As an application, we give apparently the first generalization bound in the algorithmic stability setting that holds for unbounded loss functions. We furthermore extend our concentration inequality to strongly mixing processes."]]></description>
<dc:subject>concentration_of_measure stability_of_learning learning_theory probability kontorovich.aryeh kith_and_kin re:XV_for_mixing re:your_favorite_dsge_sucks have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2636de015e38/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:concentration_of_measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stability_of_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kontorovich.aryeh"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kith_and_kin"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://economics.mit.edu/files/6988">
    <title>The Stability of General Equilibrium - What Do We Know and Why Is It Important? (Fisher, 2010)</title>
    <dc:date>2013-07-10T17:43:13+00:00</dc:date>
    <link>http://economics.mit.edu/files/6988</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Fisher's account of the problem and his own work on attacking it.  Memo to self: get hold of Fisher's 1983 book.]]></description>
<dc:subject>economics dynamical_systems distributed_systems have_read via:rortybomb in_NB re:your_favorite_dsge_sucks tracked_down_references fisher.franklin_m.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:66ee286a1b5e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:dynamical_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:distributed_systems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:rortybomb"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:tracked_down_references"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fisher.franklin_m."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.tandfonline.com/doi/abs/10.1080/01621459.2013.763726#.UdRPihbPUlM">
    <title>Taylor &amp; Francis Online :: Oracally Efficient Two-Step Estimation of Generalized Additive Model - Journal of the American Statistical Association - Volume 108, Issue 502</title>
    <dc:date>2013-07-03T16:36:08+00:00</dc:date>
    <link>http://www.tandfonline.com/doi/abs/10.1080/01621459.2013.763726#.UdRPihbPUlM</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The generalized additive model (GAM) is a multivariate nonparametric regression tool for non-Gaussian responses including binary and count data. We propose a spline-backfitted kernel (SBK) estimator for the component functions and the constant, which are oracally efficient under weak dependence. The SBK technique is both computationally expedient and theoretically reliable, thus usable for analyzing high-dimensional time series. Inference can be made on component functions based on asymptotic normality. Simulation evidence strongly corroborates the asymptotic theory. The method is applied to estimate insolvent probability and to obtain higher accuracy ratio than a previous study."]]></description>
<dc:subject>to:NB time_series additive_models statistics high-dimensional_statistics smoothing to_read re:your_favorite_dsge_sucks to_teach:undergrad-ADA regression nonparametrics hardle.wolfgang</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:daeb5e76a0f6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:additive_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:high-dimensional_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:smoothing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:hardle.wolfgang"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://noahpinionblog.blogspot.com/2013/05/what-can-you-do-with-dsge-model.html">
    <title>Noahpinion: What can you do with a DSGE model?</title>
    <dc:date>2013-05-28T18:50:33+00:00</dc:date>
    <link>http://noahpinionblog.blogspot.com/2013/05/what-can-you-do-with-dsge-model.html</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>macroeconomics prediction bad_data_analysis smith.noah re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:4bb6fa35d2c9/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:smith.noah"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1305.5882">
    <title>[1305.5882] Limit theorems for kernel density estimators under dependent samples</title>
    <dc:date>2013-05-28T17:29:40+00:00</dc:date>
    <link>http://arxiv.org/abs/1305.5882</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we construct a moment inequality for mixing dependent random variables, it is of independent interest. As applications, the consistency of the kernel density estimation is investigated. Several limit theorems are established: First, the central limit theorems for the kernel density estimator $f_{n,K}(x)$ and its distribution function are constructed. Also, the convergence rates of $\|f_{n,K}(x)-Ef_{n,K}(x)\|_{p}$ in sup-norm loss and integral $L^{p}$-norm loss are proved. Moreover, the a.s. convergence rates of the supremum of $|f_{n,K}(x)-Ef_{n,K}(x)|$ over a compact set and the whole real line are obtained. It is showed, under suitable conditions on the mixing rates, the kernel function and the bandwidths, that the optimal rates for i.i.d. random variables are also optimal for dependent ones."

--- The "to_teach" is really "to_mention"]]></description>
<dc:subject>density_estimation statistical_inference_for_stochastic_processes statistics time_series to_teach:undergrad-ADA re:your_favorite_dsge_sucks in_NB kernel_smoothing</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:51572fa4a6a3/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:density_estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:kernel_smoothing"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://users.cecs.anu.edu.au/~williams/papers/P85.pdf">
    <title>Structural Risk Minimization over Data-Dependent Hierarchies</title>
    <dc:date>2013-04-30T16:20:12+00:00</dc:date>
    <link>http://users.cecs.anu.edu.au/~williams/papers/P85.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The paper introduces some generalizations of Vapnik’s method of structural risk min- imisation (SRM). As well as making explicit some of the details on SRM, it provides a result that allows one to trade off errors on the training sample against improved general- ization performance. It then considers the more general case when the hierarchy of classes is chosen in response to the data. A result is presented on the generalization performance of classifiers with a “large margin”. This theoretically explains the impressive generaliza- tion performance of the maximal margin hyperplane algorithm of Vapnik and co-workers (which is the basis for their support vector machines). The paper concludes with a more general result in terms of “luckiness” functions, which provides a quite general way for ex- ploiting serendipitous simplicity in observed data to obtain better prediction accuracy from small training sets. Four examples are given of such functions, including the VC dimension measured on the sample."]]></description>
<dc:subject>learning_theory structural_risk_minimization classifiers vc-dimension re:your_favorite_dsge_sucks have_read to:blog in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a5e762a44ed5/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:structural_risk_minimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:classifiers"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:vc-dimension"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:blog"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://noahpinionblog.blogspot.com/2013/03/the-swamp-of-dsge-despair.html">
    <title>Noahpinion: The swamps of DSGE despair</title>
    <dc:date>2013-03-29T16:22:08+00:00</dc:date>
    <link>http://noahpinionblog.blogspot.com/2013/03/the-swamp-of-dsge-despair.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Shorter Noah: With notably rare exceptions, economics is a progressive scientific discipline.]]></description>
<dc:subject>economics macroeconomics re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e6b88d0c37bf/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/knowledge/isbn/item6852611/?site_locale=en_US">
    <title>Transforming Modern Macroeconomics: Exploring Disequilibrium Microfoundations, 1956–2003</title>
    <dc:date>2013-02-09T19:32:50+00:00</dc:date>
    <link>http://www.cambridge.org/us/knowledge/isbn/item6852611/?site_locale=en_US</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book tells the story of the search for disequilibrium micro-foundations for macroeconomic theory, from the disequilibrium theories of Patinkin, Clower, and Leijonhufvud to recent dynamic stochastic general equilibrium models with imperfect competition. Placing this search against the background of wider developments in macroeconomics, the authors contend that this was never a single research program, but involved economists with very different aims who developed the basic ideas about quantity constraints, spillover effects, and coordination failures in different ways. The authors contrast this with the equilibrium, market-clearing approach of Phelps and Lucas, arguing that equilibrium theories simply assumed away the problems that had motivated the disequilibrium literature. Although market-clearing models came to dominate macroeconomics, disequilibrium theories never went away and continue to exert an important influence on the subject. Although this book focuses on one strand in modern macroeconomics, it is crucial to understanding the origins of modern macroeconomic theory."]]></description>
<dc:subject>books:noted economics macroeconomics re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:abc908d35fce/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.bj/1358531747">
    <title>Zhao , Li : Inference for modulated stationary processes</title>
    <dc:date>2013-01-20T21:25:52+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.bj/1358531747</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We study statistical inferences for a class of modulated stationary processes with time-dependent variances. Due to non-stationarity and the large number of unknown parameters, existing methods for stationary, or locally stationary, time series are not applicable. Based on a self-normalization technique, we address several inference problems, including a self-normalized central limit theorem, a self-normalized cumulative sum test for the change-point problem, a long-run variance estimation through blockwise self-normalization, and a self-normalization-based wild bootstrap. Monte Carlo simulation studies show that the proposed self-normalization-based methods outperform stationarity-based alternatives. We demonstrate the proposed methodology using two real data sets: annual mean precipitation rates in Seoul from 1771–2000, and quarterly U.S. Gross National Product growth rates from 1947–2002."]]></description>
<dc:subject>to:NB to_read time_series statistics non-stationarity change-point_problem re:your_favorite_dsge_sucks re:growing_ensemble_project</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5b844735a73f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:non-stationarity"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:change-point_problem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:growing_ensemble_project"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.5796">
    <title>[1212.5796] On the method of typical bounded differences</title>
    <dc:date>2012-12-27T18:09:15+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.5796</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Concentration inequalities are fundamental tools in probabilistic combinatorics and theoretical computer science for proving that random functions are near their means. Of particular importance is the case where f(X) is a function of independent random variables X=(X_1, ..., X_n). Here the well known bounded differences inequality (also called McDiarmid's or Hoeffding-Azuma inequality) establishes sharp concentration if the function f does not depend too much on any of the variables. One attractive feature is that it relies on a very simple Lipschitz condition (L): it suffices to show that |f(X)-f(X')| leq c_k whenever X,X' differ only in X_k. While this is easy to check, the main disadvantage is that it considers worst-case changes c_k, which often makes the resulting bounds too weak to be useful. 
"In this paper we prove a variant of the bounded differences inequality which can be used to establish concentration of functions f(X) where (i) the typical changes are small although (ii) the worst case changes might be very large. One key aspect of this inequality is that it relies on a simple condition that (a) is easy to check and (b) coincides with heuristic considerations why concentration should hold. Indeed, given an event Gamma that holds with very high probability, we essentially relax the Lipschitz condition (L) to situations where Gamma occurs. The point is that the resulting typical changes c_k are often much smaller than the worst case ones. 
"To illustrate its application we consider the reverse H-free process, where H is 2-balanced. We prove that the final number of edges in this process is concentrated, and also determine its likely value up to constant factors. This answers a question of Bollob'as and ErdH{o}s."]]></description>
<dc:subject>to_read probability concentration_of_measure re:almost_none re:your_favorite_dsge_sucks re:XV_for_mixing re:XV_for_networks in_NB deviation_inequalities</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:20c0c9aa7555/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:probability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:concentration_of_measure"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:almost_none"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_networks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:deviation_inequalities"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1212.0463">
    <title>[1212.0463] Time series forecasting: model evaluation and selection using nonparametric risk bounds</title>
    <dc:date>2012-12-04T02:14:32+00:00</dc:date>
    <link>http://arxiv.org/abs/1212.0463</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We derive generalization error bounds --- bounds on the expected inaccuracy of the predictions --- for traditional time series forecasting models. Our results hold for many standard forecasting tools including autoregressive models, moving average models, and, more generally, linear state-space models. These bounds allow forecasters to select among competing models and to guarantee that with high probability, their chosen model will perform well without making strong assumptions about the data generating process or appealing to asymptotic theory. We motivate our techniques with and apply them to standard economic and financial forecasting tools --- a GARCH model for predicting equity volatility and a dynamic stochastic general equilibrium model (DSGE), the standard tool in macroeconomic forecasting. We demonstrate in particular how our techniques can aid forecasters and policy makers in choosing models which behave well under uncertainty and mis-specification."]]></description>
<dc:subject>learning_theory self-promotion statistics statistical_inference_for_stochastic_processes economics time_series macroeconomics re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:64f25745898b/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:self-promotion"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistical_inference_for_stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/9891.html">
    <title>De Grauwe, P.: Lectures on Behavioral Macroeconomics.</title>
    <dc:date>2012-10-24T00:25:29+00:00</dc:date>
    <link>http://press.princeton.edu/titles/9891.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In mainstream economics, and particularly in New Keynesian macroeconomics, the booms and busts that characterize capitalism arise because of large external shocks. The combination of these shocks and the slow adjustments of wages and prices by rational agents leads to cyclical movements. In this book, Paul De Grauwe argues for a different macroeconomics model--one that works with an internal explanation of the business cycle and factors in agents' limited cognitive abilities. By creating a behavioral model that is not dependent on the prevailing concept of rationality, De Grauwe is better able to explain the fluctuations of economic activity that are an endemic feature of market economies. This new approach illustrates a richer macroeconomic dynamic that provides for a better understanding of fluctuations in output and inflation.
"De Grauwe shows that the behavioral model is driven by self-fulfilling waves of optimism and pessimism, or animal spirits. Booms and busts in economic activity are therefore natural outcomes of a behavioral model. The author uses this to analyze central issues in monetary policies, such as output stabilization, before extending his investigation into asset markets and more sophisticated forecasting rules. He also examines how well the theoretical predictions of the behavioral model perform when confronted with empirical data."]]></description>
<dc:subject>books:noted economics macroeconomics re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e361b927e5db/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:books:noted"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://books.nips.cc/papers/files/nips23/NIPS2010_0731.pdf">
    <title>Learning Bounds for Importance Weights</title>
    <dc:date>2012-09-03T18:41:37+00:00</dc:date>
    <link>http://books.nips.cc/papers/files/nips23/NIPS2010_0731.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper presents an analysis of importance weighting for learning from finite samples and gives a series of theoretical and algorithmic results. We point out simple cases where importance weighting can fail, which suggests the need for an analysis of the properties of this technique. We then give both upper and lower bounds for generalization with bounded importance weights and, more signifi- cantly, give learning guarantees for the more common case of unbounded impor- tance weights under the weak assumption that the second moment is bounded, a condition related to the Re ́nyi divergence of the training and test distributions. These results are based on a series of novel and general bounds we derive for un- bounded loss functions, which are of independent interest. We use these bounds to guide the definition of an alternative reweighting algorithm and report the results of experiments demonstrating its benefits. Finally, we analyze the properties of normalized importance weights which are also commonly used."

(For the generalization bounds with unbounded losses.)]]></description>
<dc:subject>learning_theory re:your_favorite_dsge_sucks mohri.meryar have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:51c847fcda76/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mohri.meryar"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://faculty-web.at.northwestern.edu/economics/gordon/GRU_Combined_090909.pdf">
    <title>Is Modern Macro or 1978-era Macro More Relevant to the Understanding of the Current Economic Crisis?</title>
    <dc:date>2012-06-26T16:56:20+00:00</dc:date>
    <link>http://faculty-web.at.northwestern.edu/economics/gordon/GRU_Combined_090909.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper differs from other recent critiques of “modern macro” based on DSGE models. It goes beyond criticizing these models for their assumptions of complete and efficient markets by proposing an alternative macroeconomic paradigm that is more suitable for tracing the links between financial bubbles and the commodity and labor markets of the real economy.
"The paper provides a fundamental critique of DSGE and the related core assumptions of modern business cycle macroeconomics. By attempting to combine sticky Calvo‐like prices in a theoretical setting that otherwise assumes that markets clear, DSGE macro becomes tangled in a web of contradictions. Once prices are sticky, markets fail to clear. Once markets fail to clear, workers are not moving back and forth on their voluntary labor supply curves, so the elasticity of such curves is irrelevant. Once markets fail to clear, firms are not sliding back and forth on their labor demand curves, and so it is irrelevant whether the price‐cost markup (i.e., slope of the labor demand curve) is negative or positive.
"The paper resurrects “1978‐era” macroeconomics that combines non‐market‐clearing aggregate demand based on incomplete price adjustment, together with a supply‐side invented in the mid‐1970s that _recognizes the co‐existence of flexible auction‐market prices for commodities like oil and sticky prices for the remaining non‐oil economy_. As combined in 1978‐era theories, empirical work, and pioneering intermediate macro textbooks, this merger of demand and supply resulted in a well‐articulated dynamic aggregate demand‐supply model that has stood the test of time in explaining both the multiplicity of links between the financial and real economies, as well as why inflation and unemployment can be both negatively and positively correlated.
"Along the way, the paper goes beyond most recent accounts of the worldwide economic crisis by pointing out numerous similarities between the leverage cycles of 1927‐29 and 2003‐06, particularly parallel regulatory failings in both episodes, and it links tightly the empirical lack of realism in the demand and supply sides of modern DSGE models with the empirical reality that has long been built into the 1978‐era paradigm resurrected here."]]></description>
<dc:subject>in_NB economics macroeconomics re:your_favorite_dsge_sucks financial_crisis_of_2007--</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:c1794d44fd47/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macroeconomics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_crisis_of_2007--"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://riscd2.eco.ub.es/~josepgon/documents/Felipe_Fisher.pdf">
    <title>AGGREGATION IN PRODUCTION FUNCTIONS: WHAT APPLIED ECONOMISTS SHOULD KNOW</title>
    <dc:date>2012-06-18T19:55:31+00:00</dc:date>
    <link>http://riscd2.eco.ub.es/~josepgon/documents/Felipe_Fisher.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper surveys the theoretical literature on aggregation of production functions. The objective is to make neoclassical economists aware of the insurmountable aggregation problems and their implications. We refer to both the Cambridge capital controversies and the aggregation conditions. The most salient results are summarized, and the problems that economists should be aware of from incorrect aggregation are discussed. The most important conclusion is that the conditions under which a well-behaved aggregate production function can be derived from micro production functions are so stringent that it is difficult to believe that actual economies satisfy them. Therefore, aggregate production functions do not have a sound theoretical foundation. For practical purposes this means that while generating GDP, for example, as the sum of the components of aggregate demand (or through the production or income sides of the economy) is correct, thinking of GDP as GDP = F(K, L), where K and L are aggregates of capital and labor, respectively, and F(•) is a well-defined neoclassical function, is most likely incorrect. Likewise, thinking of aggregate investment as a well-defined addition to
‘capital’ in production is also a mistake. The paper evaluates the standard reasons given by economists for continuing to use aggregate production functions in theoretical and applied work, and concludes that none of them provides a valid argument."

--- They are not altogether fair to the instrumentalist, it-works-doesn't-it, defense.  (I'm not saying that defense is right, just that they don't really treat it fairly, which would involve looking into how aggregate production functions are supposed to work, and assessing the evidence that they do in fact, do those jobs well.)]]></description>
<dc:subject>in_NB economics macro_from_micro re:your_favorite_dsge_sucks via:crooked_timber econometrics cobb-douglas_production_functions have_read fisher.franklin_m.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:31f323baf3f8/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:macro_from_micro"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:crooked_timber"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cobb-douglas_production_functions"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:fisher.franklin_m."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.cambridge.org/us/knowledge/isbn/item5759368/?site_locale=en_US">
    <title>Introduction to Computable General Equilibrium Models - Academic and Professional Books - Cambridge University Press</title>
    <dc:date>2012-06-02T14:42:18+00:00</dc:date>
    <link>http://www.cambridge.org/us/knowledge/isbn/item5759368/?site_locale=en_US</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Computable general equilibrium (CGE) models are widely used by governmental organizations and academic institutions to analyze the economy-wide effects of events such as climate change, tax policies, and immigration. This book provides a practical, how-to guide to CGE models suitable for use at the undergraduate college level. Its introductory level distinguishes it from other available books and articles on CGE models. The book provides intuitive and graphical explanations of the economic theory that underlies a CGE model and includes many examples and hands-on modeling exercises. It may be used in courses on economics principles, microeconomics, macroeconomics, public finance, environmental economics, and international trade and finance, because it shows students the role of theory in a realistic model of an economy. The book is also suitable for courses on general equilibrium models and research methods, and for professionals interested in learning how to use CGE models."

- The mathematical and conceptual level here is shockingly low.]]></description>
<dc:subject>economics simulation re:your_favorite_dsge_sucks have_read re:in_soviet_union_optimization_problem_solves_you</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bde9fd3e7a0c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:in_soviet_union_optimization_problem_solves_you"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1338515139">
    <title>Lecué , Mendelson : General nonexact oracle inequalities for classes with a subexponential envelope</title>
    <dc:date>2012-06-01T14:12:36+00:00</dc:date>
    <link>http://projecteuclid.org/DPubS?service=UI&amp;version=1.0&amp;verb=Display&amp;handle=euclid.aos/1338515139</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We show that empirical risk minimization procedures and regularized empirical risk minimization procedures satisfy nonexact oracle inequalities in an unbounded framework, under the assumption that the class has a subexponential envelope function. The main novelty, in addition to the boundedness assumption free setup, is that those inequalities can yield fast rates even in situations in which exact oracle inequalities only hold with slower rates.
"We apply these results to show that procedures based on $ell_{1}$ and nuclear norms regularization functions satisfy oracle inequalities with a residual term that decreases like $1/n$ for every $L_{q}$-loss functions ($qgeq2$), while only assuming that the tail behavior of the input and output variables are well behaved. In particular, no RIP type of assumption or “incoherence condition” are needed to obtain fast residual terms in those setups. We also apply these results to the problems of convex aggregation and model selection."

This looks awesome.]]></description>
<dc:subject>to_read learning_theory model_selection statistics re:your_favorite_dsge_sucks re:XV_for_mixing ensemble_methods lecue.guillaume mendelson.shahar in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:723d702bf01d/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:XV_for_mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ensemble_methods"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:lecue.guillaume"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mendelson.shahar"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.aeaweb.org/articles.php?doi=10.1257/jep.26.2.189">
    <title>Using Internet Data for Economic Research</title>
    <dc:date>2012-05-08T19:22:12+00:00</dc:date>
    <link>http://www.aeaweb.org/articles.php?doi=10.1257/jep.26.2.189</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The data used by economists can be broadly divided into two categories. First, structured datasets arise when a government agency, trade association, or company can justify the expense of assembling records. The Internet has transformed how economists interact with these datasets by lowering the cost of storing, updating, distributing, finding, and retrieving this information. Second, some economic researchers affirmatively collect data of interest. For researcher-collected data, the Internet opens exceptional possibilities both by increasing the amount of information available for researchers to gather and by lowering researchers' costs of collecting information. In this paper, I explore the Internet's new datasets, present methods for harnessing their wealth, and survey a sampling of the research questions these data help to answer. The first section of this paper discusses "scraping" the Internet for data—that is, collecting data on prices, quantities, and key characteristics that are already available on websites but not yet organized in a form useful for economic research. A second part of the paper considers online experiments, including experiments that the economic researcher observes but does not control (for example, when Amazon or eBay alters site design or bidding rules); and experiments in which a researcher participates in design, including those conducted in partnership with a company or website, and online versions of laboratory experiments. Finally, I discuss certain limits to this type of data collection, including both "terms of use" restrictions on websites and concerns about privacy and confidentiality."]]></description>
<dc:subject>to:NB economics data_sets web re:your_favorite_dsge_sucks</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d3301a184de7/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to:NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:data_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:web"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.numdam.org/item?id=AIHPB_1995__31_2_393_0">
    <title>Doukhan, Massart, Rio: Invariance principles for absolutely regular empirical processes</title>
    <dc:date>2012-02-24T05:15:01+00:00</dc:date>
    <link>http://www.numdam.org/item?id=AIHPB_1995__31_2_393_0</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>empirical_processes stochastic_processes mixing central_limit_theorem to_read re:your_favorite_dsge_sucks in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:227ed863c23e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:empirical_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:stochastic_processes"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:mixing"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:central_limit_theorem"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1202.4294">
    <title>[1202.4294] Prediction of quantiles by statistical learning and application to GDP forecasting</title>
    <dc:date>2012-02-21T03:43:03+00:00</dc:date>
    <link>http://arxiv.org/abs/1202.4294</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator (also known as Exponentially Weighted aggregate) is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of Koenker and Bassett (1978), this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results."]]></description>
<dc:subject>to_read prediction confidence_sets learning_theory re:your_favorite_dsge_sucks re:growing_ensemble_project in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:711f1ae24c8f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:prediction"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:confidence_sets"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:your_favorite_dsge_sucks"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:growing_ensemble_project"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
</rdf:RDF>