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    <description>recent bookmarks from cshalizi</description>
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  </channel><item rdf:about="https://www.nber.org/papers/w33962">
    <title>Uncertainty in Empirical Economics | NBER</title>
    <dc:date>2025-07-02T18:36:14+00:00</dc:date>
    <link>https://www.nber.org/papers/w33962</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Econometricians invest substantial effort in constructing standard errors that yield valid inference under a hypothetical data-generating process. This paper asks a fundamental question: Are the uncertainty statements reported by applied researchers consistent with empirical frequencies? The short answer is no. Drawing on the forecasting literature, we predict estimates from “new” studies using estimates from corresponding baseline studies. By doing this across a large number of study groups and linking parameters through a hierarchical model, we compare stated probabilities to observed empirical frequencies. Alignment occurs only under limited external validity, namely, that the studies estimate different parameters."]]></description>
<dc:subject>to:NB bad_data_analysis econometrics calibration</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2cd96b2a0835/</dc:identifier>
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<item rdf:about="https://www.cambridge.org/core/journals/journal-of-economic-history/article/we-do-not-know-the-population-of-every-country-in-the-world-for-the-past-two-thousand-years/D747DDC6E499C799B0471DBE33FEB0BB">
    <title>We Do Not Know the Population of Every Country in the World for the Past Two Thousand Years | The Journal of Economic History | Cambridge Core</title>
    <dc:date>2025-03-10T14:07:54+00:00</dc:date>
    <link>https://www.cambridge.org/core/journals/journal-of-economic-history/article/we-do-not-know-the-population-of-every-country-in-the-world-for-the-past-two-thousand-years/D747DDC6E499C799B0471DBE33FEB0BB</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Economists have reported results based on populations for every country in the world for the past two thousand years. The source, McEvedy and Jones’ Atlas of World Population History, includes many estimates that are little more than guesses and that do not reflect research since 1978. McEvedy and Jones often infer population sizes from their view of a particular economy, making their estimates poor proxies for economic growth. Their rounding means their measurement error is not “classical.” Some economists augment that error by disaggregating regions in unfounded ways. Econometric results that rest on McEvedy and Jones are unreliable.
"“… we haven’t just pulled the figures out of the sky. Well, not often.”
"—McEvedy and Jones (1978, p. 11)"

--- I want to teach this to The Kids, but it simultaneously expects too much historical knowledge on their part, and would make too many of them nihilists about social science.]]></description>
<dc:subject>to:NB have_read history economic_history econometrics social_measurement bad_data_collection demography bad_data_analysis to_teach:undergrad-ADA tab_closure</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b68c7ada7248/</dc:identifier>
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<item rdf:about="https://www.degruyter.com/document/doi/10.1515/9780691256740/html">
    <title>The Data Economy</title>
    <dc:date>2025-03-02T14:51:28+00:00</dc:date>
    <link>https://www.degruyter.com/document/doi/10.1515/9780691256740/html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The most valuable firms in the global economy are valued largely for their data. Amazon, Apple, Google, and others have proven the competitive advantage of a good data set. And yet despite the growing importance of data as a strategic asset, modern economic theory neglects its role. In this book, Isaac Baley and Laura Veldkamp draw on a range of theoretical frameworks at the research frontier in macroeconomics and finance to model and measure data economies. Starting from the premise that data is digitized information that facilitates prediction and reduces uncertainty, Baley and Veldkamp uncover the ways that firm-level data choices resonate throughout the broader macroeconomic and financial landscapes.
"With The Data Economy, Baley and Veldkamp put forward a broad research agenda with a formal yet accessible approach, offering an analysis of the data economy and its welfare effects that will be of interest to practitioners, researchers, and graduate students. The tools presented, many of them information-related methods from macroeconomics and finance, are theoretical but introduced with careful attention to how they can inform or enable measurement. Applications include assessing the economic worth of data and unraveling its influence on the structure of production, inflation, and pricing dynamics; firm and investor behavior; advertising; market power; and asset pricing. Baley and Veldkamp bring readers to the cutting edge of this novel research area, equipping them to formulate their own theoretical advances and policy analysis."]]></description>
<dc:subject>to:NB economics macroeconomics finance econometrics information_theory books:noted</dc:subject>
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<dc:identifier>https://pinboard.in/u:cshalizi/b:c7f09031cfe2/</dc:identifier>
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<item rdf:about="https://academic.oup.com/ej/article-abstract/127/605/F236/5069452?login=false">
    <title>Power of Bias in Economics Research | The Economic Journal | Oxford Academic</title>
    <dc:date>2023-05-02T20:10:08+00:00</dc:date>
    <link>https://academic.oup.com/ej/article-abstract/127/605/F236/5069452?login=false</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We investigate two critical dimensions of the credibility of empirical economics research: statistical power and bias. We survey 159 empirical economics literatures that draw upon 64,076 estimates of economic parameters reported in more than 6,700 empirical studies. Half of the research areas have nearly 90% of their results under‐powered. The median statistical power is 18%, or less. A simple weighted average of those reported results that are adequately powered (power ≥ 80%) reveals that nearly 80% of the reported effects in these empirical economics literatures are exaggerated; typically, by a factor of two and with one‐third inflated by a factor of four or more."

--- Power's really a function, not a number, so where's "18%" come from?  Is that the power to detect an effect of the magnitude estimated (a little weirdly recursive...), or some standard-size magnitude?
--- ETA after reading: Yes, for each area of economics they do a supposedly-robust meta-estimate of the effect size, and try to work out the power to detect an effect that big.]]></description>
<dc:subject>to:NB economics econometrics statistics hypothesis_testing re:neutral_model_of_inquiry estimation have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2c2f32247eac/</dc:identifier>
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<item rdf:about="https://www.sciencedirect.com/science/article/pii/S0169721811004072?casa_token=lgieHzuFKHQAAAAA:BAsYjUbrbT38b3WrBiwDqJTh00eeKLaayfIDdbCcrJbDurnQq94qV1BCjkvF-n-qgNt6tA2I">
    <title>Decomposition Methods in Economics - ScienceDirect</title>
    <dc:date>2022-07-09T19:01:21+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S0169721811004072?casa_token=lgieHzuFKHQAAAAA:BAsYjUbrbT38b3WrBiwDqJTh00eeKLaayfIDdbCcrJbDurnQq94qV1BCjkvF-n-qgNt6tA2I</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This chapter provides a comprehensive overview of decomposition methods that have been developed since the seminal work of Oaxaca and Blinder in the early 1970s. These methods are used to decompose the difference in a distributional statistic between two groups, or its change over time, into various explanatory factors. While the original work of Oaxaca and Blinder considered the case of the mean, our main focus is on other distributional statistics besides the mean, such as quantiles, the Gini coefficient or the variance. We discuss the assumptions required for identifying the different elements of the decomposition, as well as various estimation methods proposed in the literature. We also illustrate how these methods work in practice by discussing existing applications and working through a set of empirical examples throughout the paper."]]></description>
<dc:subject>to:NB econometrics inequality to_teach:statistics_of_inequality_and_discrimination via:donsker_class</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d426911976e4/</dc:identifier>
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<item rdf:about="https://press.uchicago.edu/ucp/books/book/chicago/B/bo136254067">
    <title>Big Data for Twenty-First-Century Economic Statistics, Abraham, Jarmin, Moyer</title>
    <dc:date>2022-05-11T17:12:03+00:00</dc:date>
    <link>https://press.uchicago.edu/ucp/books/book/chicago/B/bo136254067</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The existing infrastructure for the production of key economic statistics relies heavily on data collected through sample surveys and periodic censuses, together with administrative records generated in connection with tax administration. The increasing difficulty of obtaining survey and census responses threatens the viability of existing data collection approaches. The growing availability of new sources of Big Data—such as scanner data on purchases, credit card transaction records, payroll information, and prices of various goods scraped from the websites of online sellers—has changed the data landscape. These new sources of data hold the promise of allowing the statistical agencies to produce more accurate, more disaggregated, and more timely economic data to meet the needs of policymakers and other data users. This volume documents progress made toward that goal and the challenges to be overcome to realize the full potential of Big Data in the production of economic statistics. It describes the deployment of Big Data to solve both existing and novel challenges in economic measurement, and it will be of interest to statistical agency staff, academic researchers, and serious users of economic statistics."]]></description>
<dc:subject>to:NB data_analysis econometrics computational_statistics social_measurement books:noted books:suggest_to_library</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:e7a4adbeaf79/</dc:identifier>
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<item rdf:about="https://www.nber.org/papers/w29691">
    <title>Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey | NBER</title>
    <dc:date>2022-03-19T23:06:05+00:00</dc:date>
    <link>https://www.nber.org/papers/w29691</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Linear regressions with period and group fixed effects are widely used to estimate policies' effects: 26 of the 100 most cited papers published by the American Economic Review from 2015 to 2019 estimate such regressions. It has recently been show that those regressions may produce misleading estimates, if the policy's effect is heterogeneous between groups or over time, as is often the case. This survey reviews a fast-growing literature that documents this issue, and that proposes alternative estimators robust to heterogeneous effects."

--- For "recently been shown", read "was obvious if you stopped to think about it just one moment", but more joy over the returned prodigal, etc., etc.]]></description>
<dc:subject>to:NB linear_regression causal_inference econometrics re:TALR</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:430867822da3/</dc:identifier>
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<item rdf:about="https://www.nber.org/papers/w29820">
    <title>Systemic Discrimination: Theory and Measurement | NBER</title>
    <dc:date>2022-03-15T13:21:46+00:00</dc:date>
    <link>https://www.nber.org/papers/w29820</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Economics tends to define and measure discrimination as disparities stemming from the direct (causal) effects of protected group membership. But work in other fields notes that such measures are incomplete, as they can miss important systemic (i.e. indirect) channels. For example, racial disparities in criminal records due to discrimination in policing can lead to disparate outcomes for equally-qualified job applicants despite a race-neutral hiring rule. We develop new tools for modeling and measuring both direct and systemic forms of discrimination. We define systemic discrimination as emerging from group-based differences in non-group characteristics, conditional on a measure of individual qualification. We formalize sources of systemic discrimination as disparities in signaling technologies and opportunities for skill development. Notably, standard tools for measuring direct discrimination, such as audit or correspondence studies, cannot detect systemic discrimination. We propose a measure of systemic discrimination based on a novel decomposition of total discrimination—disparities that condition on underlying qualification—into direct and systemic components. This decomposition highlights the type of data needed to measure systemic discrimination and guides identification strategies in both observational and (quasi-)experimental data. We illustrate these tools in two hiring experiments. Our findings highlight how discrimination in one domain, due to either accurate beliefs or bias, can drive persistent disparities through systemic channels even when direct discrimination is eliminated."

--- I am going to be very, very interested to <strike>see how the way they set up their decomposition presupposes their conclusions</strike> examine their identification assumptions.]]></description>
<dc:subject>to:NB discrimination econometrics causal_inference statistics to_teach:statistics_of_inequality_and_discrimination color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:acabb2f1cc5f/</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:discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://direct.mit.edu/rest/article-abstract/101/5/743/58556/Choosing-among-Regularized-Estimators-in-Empirical?redirectedFrom=fulltext">
    <title>Choosing among Regularized Estimators in Empirical Economics: The Risk of Machine Learning | The Review of Economics and Statistics | MIT Press</title>
    <dc:date>2022-03-14T18:19:21+00:00</dc:date>
    <link>https://direct.mit.edu/rest/article-abstract/101/5/743/58556/Choosing-among-Regularized-Estimators-in-Empirical?redirectedFrom=fulltext</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Many settings in empirical economics involve estimation of a large number of parameters. In such settings, methods that combine regularized estimation and data-driven choices of regularization parameters are useful. We provide guidance to applied researchers on the choice between regularized estimators and data-driven selection of regularization parameters. We characterize the risk and relative performance of regularized estimators as a function of the data-generating process and show that data-driven choices of regularization parameters yield estimators with risk uniformly close to the risk attained under the optimal (unfeasible) choice of regularization parameters. We illustrate using examples from empirical economics."]]></description>
<dc:subject>to:NB econometrics estimation statistics learning_theory to_teach:childs_garden_of_statistical_learning_theory re:HEAS downloaded cross-validation</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:765356ad4562/</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:econometrics"/>
	<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:learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:childs_garden_of_statistical_learning_theory"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:HEAS"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:downloaded"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:cross-validation"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/wber/article-abstract/36/1/19/6333254?redirectedFrom=fulltext">
    <title>Wealth Inequality in South Africa, 1993–2017 | The World Bank Economic Review | Oxford Academic</title>
    <dc:date>2022-03-14T18:06:34+00:00</dc:date>
    <link>https://academic.oup.com/wber/article-abstract/36/1/19/6333254?redirectedFrom=fulltext</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article estimates the distribution of personal wealth in South Africa by combining microdata covering the universe of income tax returns, household surveys, and macroeconomic balance sheet statistics. South Africa is characterized by unparalleled levels of wealth concentration. The top 10 percent own 86 percent of aggregate wealth and the top 0.1 percent close to one-third. The top 0.01 percent of the distribution (3,500 individuals) concentrate 15 percent of household net worth, more than the bottom 90 percent as a whole. Such levels of inequality can be accounted for in all forms of assets at the top end, including housing, pension funds, and financial assets. There has been no sign of decreasing inequality since the end of apartheid."]]></description>
<dc:subject>to:NB inequality econometrics south_africa to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:8a69e852f4b4/</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:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:south_africa"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nber.org/papers/w29374">
    <title>Top Wealth in America: New Estimates and Implications for Taxing the Rich | NBER</title>
    <dc:date>2021-10-18T15:55:48+00:00</dc:date>
    <link>https://www.nber.org/papers/w29374</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper uses administrative tax data to estimate top wealth in the United States. We assemble new data that links people to their sources of capital income and develop new methods to estimate the degree of return heterogeneity within asset classes. Disaggregated fixed income data reveal that rich individuals earn much more of their interest income in higher-yielding forms, and have much greater exposure to credit risk. Consequently, in recent years, the interest rate on fixed income at the top is approximately three times higher than the average. Using firm-level characteristics to value firms, we find that twenty percent of total pass-through business wealth accrues to those with losses. We combine this new data on fixed income and pass-through business returns with refined estimates of C-corporation equity, housing, and pension wealth to deliver new capitalized wealth estimates. Our approach---which builds on Saez and Zucman (2016) and Bricker, Henriques, and Hansen (2018)---reduces bias because wealth and rates of return are correlated. From 1989 to 2016, the top 1%, 0.1%, and 0.01% wealth shares increased by 7.6, 5.1, and 3.0 percentage points, respectively, to 31.5%, 15.0%, and 7.0%. While these changes are less dramatic than some prior estimates, wealth is very concentrated: the top 1% holds nearly as much wealth as either the bottom 90% or the "P90-99" class. We discuss implications for income inequality measures, capital tax policy, and savings behavior."]]></description>
<dc:subject>to:NB economics inequality econometrics class_struggles_in_america to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:587354373f1b/</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:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:class_struggles_in_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-060220-023547">
    <title>Sufficient Statistics Revisited | Annual Review of Economics</title>
    <dc:date>2021-08-06T15:30:56+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-060220-023547</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This article reviews and generalizes the sufficient statistics approach to policy evaluation. The idea of the approach is that the welfare effect of policy changes can be expressed in terms of estimable reduced-form elasticities, allowing for policy evaluation without estimating the structural primitives of fully specified models. The approach relies on three assumptions: that policy changes are small, that government policy is the only source of market imperfection, and that a set of high-level restrictions on the environment and on preferences can be used to reduce the number of elasticities to be estimated. We generalize the approach in all three dimensions. It is possible to develop transparent sufficient statistics formulas under very general conditions, but the estimation requirements increase greatly. Starting from such general formulas elucidates that feasible empirical implementations are in fact structural approaches."]]></description>
<dc:subject>to:NB economic_policy econometrics sufficiency</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:07177d96e802/</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:economic_policy"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:sufficiency"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://osf.io/preprints/socarxiv/gd2t6">
    <title>SocArXiv Papers | How Robust are Estimates of Intergenerational Income Mobility?</title>
    <dc:date>2021-07-28T04:14:09+00:00</dc:date>
    <link>https://osf.io/preprints/socarxiv/gd2t6</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Rising inequalities in rich countries have led to concerns that the economic ladder is getting harder to climb. It is well established that intergenerational income mobility is lower in countries with high inequality, but research on trends in mobility finds conflicting results. Motivated by this uncertainty, we ask: how important are choices of specification for levels and trends in intergenerational income associations? We use Swedish data on cohorts born 1958–1977 and their parents. Varying how, when and for whom income is measured, we estimate 1,658,880 different associations (82,944 specifications across 20 cohorts). Our results reveal that model choice is an underrecognized source of variation in intergenerational mobility research. The most consistent contributor to trends is the advancement of women in the labor market, which leads to increased persistence in women’s earnings and the family income of both men and women. Depending on specification, it is possible to conclude that income mobility is increasing, decreasing, or remaining flat. Despite variability, our results are broadly consistent with the received view that the level of mobility in Sweden is high in a comparative perspective."]]></description>
<dc:subject>econometrics misspecification model_checking inequality transmission_of_inequality in_NB to_teach:statistics_of_inequality_and_discrimination</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:73035bc7f32c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_checking"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:transmission_of_inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:statistics_of_inequality_and_discrimination"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2107.09736">
    <title>[2107.09736] Recent Developments in Inference: Practicalities for Applied Economics</title>
    <dc:date>2021-07-23T02:52:24+00:00</dc:date>
    <link>https://arxiv.org/abs/2107.09736</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide a review of recent developments in the calculation of standard errors and test statistics for statistical inference. While much of the focus of the last two decades in economics has been on generating unbiased coefficients, recent years has seen a variety of advancements in correcting for non-standard standard errors. We synthesize these recent advances in addressing challenges to conventional inference, like heteroskedasticity, clustering, serial correlation, and testing multiple hypotheses. We also discuss recent advancements in numerical methods, such as the bootstrap, wild bootstrap, and randomization inference. We make three specific recommendations. First, applied economists need to clearly articulate the challenges to statistical inference that are present in data as well as the source of those challenges. Second, modern computing power and statistical software means that applied economists have no excuse for not correctly calculating their standard errors and test statistics. Third, because complicated sampling strategies and research designs make it difficult to work out the correct formula for standard errors and test statistics, we believe that in the applied economics profession it should become standard practice to rely on asymptotic refinements to the distribution of an estimator or test statistic via bootstrapping. Throughout, we reference built-in and user-written Stata commands that allow one to quickly calculate accurate standard errors and relevant test statistics."]]></description>
<dc:subject>to:NB statistics econometrics bootstrap</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f27367abbbc8/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bootstrap"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1808.07861">
    <title>[1808.07861] On model selection criteria for climate change impact studies</title>
    <dc:date>2021-06-10T02:12:02+00:00</dc:date>
    <link>https://arxiv.org/abs/1808.07861</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Climate change impact studies inform policymakers on the estimated damages of future climate change on economic, health and other outcomes. In most studies, an annual outcome variable is observed, e.g. agricultural yield, annual mortality or gross domestic product, along with a higher-frequency regressor, e.g. daily temperature. While applied researchers tend to consider multiple models to characterize the relationship between the outcome and the high-frequency regressor, to inform policy a choice between the damage functions implied by the different models has to be made. This paper formalizes the model selection problem in this empirical setting and provides conditions for the consistency of Monte Carlo Cross-validation and generalized information criteria. A simulation study illustrates the theoretical results and points to the relevance of the signal-to-noise ratio for the finite-sample behavior of the model selection criteria. Two empirical applications with starkly different signal-to-noise ratios illustrate the practical implications of the formal analysis on model selection criteria provided in this paper."]]></description>
<dc:subject>to:NB model_selection climate_change econometrics statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:af3a5c918559/</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:climate_change"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</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://arxiv.org/abs/2103.12374">
    <title>[2103.12374] What Do We Get from a Two-Way Fixed Effects Estimator? Implications from a General Numerical Equivalence</title>
    <dc:date>2021-05-12T18:26:22+00:00</dc:date>
    <link>https://arxiv.org/abs/2103.12374</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper shows that a two-way fixed effects (TWFE) estimator is a weighted average of first-difference (FD) estimators with different gaps between periods, generalizing a well-known equivalence theorem in a two-period panel. Exploiting the identity, I clarify required conditions for the causal interpretation of the TWFE estimator. I highlight its several limitations and propose a generalized estimator that overcomes the limitations. An empirical application on the estimates of the minimum wage effects illustrates that recognizing the numerical equivalence and making use of the generalized estimator enable more transparent understanding of what we get from the TWFE estimator."

--- I should probably add a chapter on fixed and random effects to TALR, in my copious spare time.]]></description>
<dc:subject>to:NB causal_inference econometrics linear_regression to_teach:linear_models re:TALR</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:52dd58167535/</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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:TALR"/>
</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://arxiv.org/abs/1812.02276">
    <title>[1812.02276] Identifying the Effect of Persuasion</title>
    <dc:date>2021-04-12T03:08:24+00:00</dc:date>
    <link>https://arxiv.org/abs/1812.02276</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We set up an econometric model of persuasion and study identification of key parameters under various scenarios of data availability. We find that a commonly used measure of persuasion does not estimate the persuasion rate of any population in general. We provide formal identification results, recommend several new parameters to estimate, and discuss their interpretation. Further, we propose methods for carrying out inference. We revisit the empirical literature on persuasion to show that the persuasive effect is highly heterogeneous. We also show that the existence of a continuous instrument opens up the possibility of point identification for the policy-relevant population."]]></description>
<dc:subject>to:NB influence econometrics social_measurement social_influence causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:bcf0284f77c0/</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:influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2005.12395">
    <title>[2005.12395] Fair Policy Targeting</title>
    <dc:date>2021-04-05T19:09:23+00:00</dc:date>
    <link>https://arxiv.org/abs/2005.12395</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities on sensitive attributes such as age, gender, or race. This paper addresses the question of the design of fair and efficient treatment allocation rules. We adopt the non-maleficence perspective of "first do no harm": we propose to select the fairest allocation within the Pareto frontier. We provide envy-freeness justifications to novel counterfactual notions of fairness. We discuss easy-to-implement estimators of the policy function, by casting the optimization into a mixed-integer linear program formulation. We derive regret bounds on the unfairness of the estimated policy function, and small sample guarantees on the Pareto frontier. Finally, we illustrate our method using an application from education economics."]]></description>
<dc:subject>algorithmic_fairness econometrics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3ee3fbe64908/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:algorithmic_fairness"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</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://doi.org/10.1093/oso/9780190900663.001.0001">
    <title>Reproducible Econometrics Using R - Oxford Scholarship</title>
    <dc:date>2021-01-16T07:03:52+00:00</dc:date>
    <link>https://doi.org/10.1093/oso/9780190900663.001.0001</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This book is designed to facilitate reproducibility in Econometrics. It does so by using open source software (R) and recently developed tools (R Markdown and bookdown) that allow the reader to engage in reproducible research. Illustrative examples are provided throughout, and a range of topics are covered. Assignments, exams, slides, and a solution manual are available for instructors."

--- I checked out the library's codex copy just before lockdown and have been slowly, but enjoyably, going through it...]]></description>
<dc:subject>to:NB books:noted to_download to_read racine.jeffrey econometrics R to_teach:undergrad-ADA to_teach:data_over_space_and_time statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:66446d8278e8/</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:to_download"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racine.jeffrey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1016/j.jeconom.2018.03.007">
    <title>The ABC of simulation estimation with auxiliary statistics - ScienceDirect</title>
    <dc:date>2020-12-13T23:18:00+00:00</dc:date>
    <link>https://doi.org/10.1016/j.jeconom.2018.03.007</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The frequentist method of simulated minimum distance (SMD) is widely used in economics to estimate complex models with an intractable likelihood. In other disciplines, a Bayesian approach known as Approximate Bayesian Computation (ABC) is far more popular. This paper connects these two seemingly related approaches to likelihood-free estimation by means of a Reverse Sampler that uses both optimization and importance weighting to target the posterior distribution. Its hybrid features enable us to analyze an ABC estimate from the perspective of SMD. We show that an ideal ABC estimate can be obtained as a weighted average of a sequence of SMD modes, each being the minimizer of the deviations between the data and the model. This contrasts with the SMD, which is the mode of the average deviations. Using stochastic expansions, we provide a general characterization of frequentist estimators and those based on Bayesian computations including Laplace-type estimators. Their differences are illustrated using analytical examples and a simulation study of the dynamic panel model."]]></description>
<dc:subject>to:NB approximate_bayesian_computation simulation-based_estimation econometrics ng.serena have_skimmed re:codename:catherine_wheel</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6135a93f9671/</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:approximate_bayesian_computation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:simulation-based_estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:ng.serena"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:codename:catherine_wheel"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.econ.yale.edu/~pah29/intro.pdf">
    <title>&quot;Structural vs. Reduced Form&quot;: Language, Confusion, and Models in Empirical Economics</title>
    <dc:date>2020-12-10T22:00:23+00:00</dc:date>
    <link>http://www.econ.yale.edu/~pah29/intro.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[I appreciate the tone of exasperatedly laying down laws that ought to be obvious.]]></description>
<dc:subject>econometrics causal_inference statistics rectification_of_names structural_equations have_read via:?</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d662e28818c6/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:rectification_of_names"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:structural_equations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:?"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2007.13888">
    <title>[2007.13888] Local Projection Inference is Simpler and More Robust Than You Think</title>
    <dc:date>2020-12-10T05:45:16+00:00</dc:date>
    <link>https://arxiv.org/abs/2007.13888</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Applied macroeconomists often compute confidence intervals for impulse responses using local projections, i.e., direct linear regressions of future outcomes on current covariates. This paper proves that local projection inference robustly handles two issues that commonly arise in applications: highly persistent data and the estimation of impulse responses at long horizons. We consider local projections that control for lags of the variables in the regression. We show that lag-augmented local projections with normal critical values are asymptotically valid uniformly over (i) both stationary and non-stationary data, and also over (ii) a wide range of response horizons. Moreover, lag augmentation obviates the need to correct standard errors for serial correlation in the regression residuals. Hence, local projection inference is arguably both simpler than previously thought and more robust than standard autoregressive inference, whose validity is known to depend sensitively on the persistence of the data and on the length of the horizon."]]></description>
<dc:subject>to:NB time_series statistics econometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:673deccff84e/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://academic.oup.com/ectj/issue/23/3">
    <title>Volume 23 Issue 3 | The Econometrics Journal: SPECIAL ISSUE ON THE METHODOLOGY AND APPLICATIONS OF STRUCTURAL DYNAMIC MODELS AND MACHINE LEARNING</title>
    <dc:date>2020-10-23T16:56:05+00:00</dc:date>
    <link>https://academic.oup.com/ectj/issue/23/3</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB to_read econometrics learning_theory</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:58c5b8f82418/</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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:learning_theory"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nber.org/papers/w27600">
    <title>Randomization in the Tropics Revisited: a Theme and Eleven Variations</title>
    <dc:date>2020-08-10T18:14:55+00:00</dc:date>
    <link>https://www.nber.org/papers/w27600</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Randomized controlled trials have been used in economics for 50 years, and intensively in economic development for more than 20. There has been a great deal of useful work, but RCTs have no unique advantages or disadvantages over other empirical methods in economics. They do not simplify inference, nor can an RCT establish causality. Many of the difficulties were recognized and explored in economics 30 years ago, but are sometimes forgotten. I review some of the most relevant issues here. The most troubling questions concern ethics, especially when very poor people are experimented on. Finding out what works, even if such a thing is possible, is in itself a deeply inadequate basis for policy"]]></description>
<dc:subject>to:NB experimental_economics causal_inference econometrics development_economics deaton.angus social_science_methodology have_read re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:677e2e17ef03/</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:experimental_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:development_economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:deaton.angus"/>
	<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:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/2004.11751">
    <title>[2004.11751] Microeconometrics with Partial Identification</title>
    <dc:date>2020-07-17T19:07:53+00:00</dc:date>
    <link>https://arxiv.org/abs/2004.11751</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This chapter reviews the microeconometrics literature on partial identification, focusing on the developments of the last thirty years. The topics presented illustrate that the available data combined with credible maintained assumptions may yield much information about a parameter of interest, even if they do not reveal it exactly. Special attention is devoted to discussing the challenges associated with, and some of the solutions put forward to, (1) obtain a tractable characterization of the values for the parameters of interest which are observationally equivalent, given the available data and maintained assumptions; (2) estimate this set of values; (3) conduct test of hypotheses and make confidence statements. The chapter reviews advances in partial identification analysis both as applied to learning (functionals of) probability distributions that are well-defined in the absence of models, as well as to learning parameters that are well-defined only in the context of particular models. A simple organizing principle is highlighted: the source of the identification problem can often be traced to a collection of random variables that are consistent with the available data and maintained assumptions. This collection may be part of the observed data or be a model implication. In either case, it can be formalized as a random set. Random set theory is then used as a mathematical framework to unify a number of special results and produce a general methodology to carry out partial identification analysis."]]></description>
<dc:subject>to:NB statistics econometrics partial_identification</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2ce2414a09c7/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:partial_identification"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nber.org/papers/w26480">
    <title>Teacher Effects on Student Achievement and Height: A Cautionary Tale</title>
    <dc:date>2020-07-13T18:23:23+00:00</dc:date>
    <link>https://www.nber.org/papers/w26480</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Estimates of teacher “value-added” suggest teachers vary substantially in their ability to promote student learning. Prompted by this finding, many states and school districts have adopted value-added measures as indicators of teacher job performance. In this paper, we conduct a new test of the validity of value-added models. Using administrative student data from New York City, we apply commonly estimated value-added models to an outcome teachers cannot plausibly affect: student height. We find the standard deviation of teacher effects on height is nearly as large as that for math and reading achievement, raising obvious questions about validity. Subsequent analysis finds these “effects” are largely spurious variation (noise), rather than bias resulting from sorting on unobserved factors related to achievement. Given the difficulty of differentiating signal from noise in real-world teacher effect estimates, this paper serves as a cautionary tale for their use in practice."]]></description>
<dc:subject>to:NB value-added_measures statistics econometrics bad_data_analysis have_skimmed trapped_in_plutos_republic value-added_measurement_in_education</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:23cf195172d5/</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:value-added_measures"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:bad_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_skimmed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:trapped_in_plutos_republic"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:value-added_measurement_in_education"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3273476">
    <title>Generalizable and Robust TV Advertising Effects by Bradley Shapiro, Günter J. Hitsch, Anna Tuchman :: SSRN</title>
    <dc:date>2020-01-09T17:43:36+00:00</dc:date>
    <link>https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3273476</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We provide generalizable and robust results on the causal sales effect of TV advertising for a large number of products in many categories. Such generalizable results provide a prior distribution that can improve the advertising decisions made by firms and the analysis and recommendations of policy makers. A single case study cannot provide generalizable results, and hence the literature provides several meta-analyses based on published case studies of advertising effects. However, publication bias results if the research or review process systematically rejects estimates of small, statistically insignificant, or “unexpected” advertising elasticities. Consequently, if there is publication bias, the results of a meta-analysis will not reflect the true population distribution of advertising effects. To provide generalizable results, we base our analysis on a large number of products and clearly lay out the research protocol used to select the products. We characterize the distribution of all estimates, irrespective of sign, size, or statistical significance. To ensure generalizability, we document the robustness of the estimates. First, we examine the sensitivity of the results to the assumptions made when constructing the data used in estimation. Second, we document whether the estimated effects are sensitive to the identification strategies that we use to claim causality based on observational data. Our results reveal substantially smaller advertising elasticities compared to the results documented in the extant literature, as well as a sizable percentage of statistically insignificant or negative estimates. If we only select products with statistically significant and positive estimates, the mean and median of the advertising effect distribution increase by a factor of about five. The results are robust to various identifying assumptions, and are consistent with both publication bias and bias due to non-robust identification strategies to obtain causal estimates in the literature."]]></description>
<dc:subject>to:NB causal_inference econometrics statistics advertising to_teach:undergrad-ADA</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6309f471803b/</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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jel.20181361">
    <title>The Identification Zoo: Meanings of Identification in Econometrics</title>
    <dc:date>2019-12-06T15:45:06+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jel.20181361</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Over two dozen different terms for identification appear in the econometrics literature, including set identification, causal identification, local identification, generic identification, weak identification, identification at infinity, and many more. This survey (i) gives a new framework unifying existing definitions of point identification; (ii) summarizes and compares the zooful of different terms associated with identification that appear in the literature; and (iii) discusses concepts closely related to identification, such as normalizations and the differences in identification between structural models and causal, reduced form models."]]></description>
<dc:subject>to:NB identifiability statistics econometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:293c1a57a463/</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:identifiability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.kellogg.northwestern.edu/faculty/gordon_b/files/fb_comparison.pdf">
    <title>A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook</title>
    <dc:date>2019-11-27T00:44:11+00:00</dc:date>
    <link>https://www.kellogg.northwestern.edu/faculty/gordon_b/files/fb_comparison.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>advertising econometrics causal_inference statistics have_read to_teach:data-mining networked_life</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:78b91ed32b9a/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.nber.org/papers/w20171.pdf">
    <title>Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment</title>
    <dc:date>2019-11-27T00:19:31+00:00</dc:date>
    <link>https://www.nber.org/papers/w20171.pdf</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB advertising causal_inference econometrics statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b455c124aec1/</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:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://thecorrespondent.com/100/the-new-dot-com-bubble-is-here-its-called-online-advertising/13228924500-22d5fd24">
    <title>The new dot com bubble is here: it’s called online advertising - The Correspondent</title>
    <dc:date>2019-11-27T00:18:04+00:00</dc:date>
    <link>https://thecorrespondent.com/100/the-new-dot-com-bubble-is-here-its-called-online-advertising/13228924500-22d5fd24</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>advertising networked_life causal_inference statistics market_failures_in_everything corporations econometrics have_read to_teach:data-mining</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:2c891d017048/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:advertising"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:networked_life"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:market_failures_in_everything"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:corporations"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data-mining"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00795">
    <title>How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions | The Review of Economics and Statistics | MIT Press Journals</title>
    <dc:date>2019-10-24T15:01:31+00:00</dc:date>
    <link>https://www.mitpressjournals.org/doi/abs/10.1162/rest_a_00795</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["We propose to use economic theories to construct shrinkage estimators that perform well when the theories' empirical implications are approximately correct but perform no worse than unrestricted estimators when the theories' implications do not hold. We implement this construction in various settings, including labor demand and wage inequality, and estimation of consumer demand. We provide asymptotic and finite sample characterizations of the behavior of the proposed estimators. Our approach is an alternative to the use of theory as something to be tested or to be imposed on estimates. Our approach complements uses of theory for identification and extrapolation."]]></description>
<dc:subject>to:NB economics econometrics statistics estimation shrinkage</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:384ae1da5083/</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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:estimation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:shrinkage"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1906.10258">
    <title>[1906.10258] Policy Targeting under Network Interference</title>
    <dc:date>2019-08-29T00:46:59+00:00</dc:date>
    <link>https://arxiv.org/abs/1906.10258</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper discusses the problem of estimating individualized treatment allocation rules under network interference. We propose a method with several appealing features for applications: we let treatment and spillover effects be heterogeneous in the population, and we construct targeting rules that exploit such heterogeneity; we accommodate for arbitrary, possibly non-linear, regression models, and we propose estimators that are robust to model misspecification; treatment allocation rules depend on an arbitrary set of individual, neighbors' and network characteristics, and we allow for general constraints on the policy function and capacity constraints on the number of treated units; the proposed methodology is valid also when only local information of the network is observed. From a theoretical perspective, we establish the first set of guarantees on the utilitarian regret under interference, and we show that it achieves the min-max optimal rate in scenarios of practical and theoretical interest. We provide a mixed-integer linear program formulation of the optimization problem, that can be solved using standard optimization routines. We discuss the empirical performance in simulations, and we illustrate our method by investigating the role of social networks in micro-finance decisions."]]></description>
<dc:subject>to:NB social_influence causal_inference network_data_analysis statistics econometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:d30fddcdd222/</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:social_influence"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:network_data_analysis"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://arxiv.org/abs/1908.06133">
    <title>[1908.06133] A model of discrete choice based on reinforcement learning under short-term memory</title>
    <dc:date>2019-08-20T15:28:28+00:00</dc:date>
    <link>https://arxiv.org/abs/1908.06133</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A family of models of individual discrete choice are constructed by means of statistical averaging of choices made by a subject in a reinforcement learning process, where the subject has short, k-term memory span. The choice probabilities in these models combine in a non-trivial, non-linear way the initial learning bias and the experience gained through learning. The properties of such models are discussed and, in particular, it is shown that probabilities deviate from Luce's Choice Axiom, even if the initial bias adheres to it. Moreover, we shown that the latter property is recovered as the memory span becomes large. 
"Two applications in utility theory are considered. In the first, we use the discrete choice model to generate binary preference relation on simple lotteries. We show that the preferences violate transitivity and independence axioms of expected utility theory. Furthermore, we establish the dependence of the preferences on frames, with risk aversion for gains, and risk seeking for losses. Based on these findings we propose next a parametric model of choice based on the probability maximization principle, as a model for deviations from expected utility principle. To illustrate the approach we apply it to the classical problem of demand for insurance."]]></description>
<dc:subject>reinforcement_learning econometrics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:ce255f97a26f/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:reinforcement_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.sciencedirect.com/science/article/pii/S2352827319300096?via%3Dihub">
    <title>Free trade and opioid overdose death in the United States - ScienceDirect</title>
    <dc:date>2019-07-29T18:47:52+00:00</dc:date>
    <link>https://www.sciencedirect.com/science/article/pii/S2352827319300096?via%3Dihub</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Opioid overdose deaths in the U.S. rose dramatically after 1999, but also exhibited substantial geographic variation. This has largely been explained by differential availability of prescription and non-prescription opioids, including heroin and fentanyl. Recent studies explore the underlying role of socioeconomic factors, but overlook the influence of job loss due to international trade, an economic phenomenon that disproportionately harms the same regions and demographic groups at the heart of the opioid epidemic. We used OLS regression and county-year level data from the Centers for Disease Controls and the Department of Labor to test the association between trade-related job loss and opioid-related overdose death between 1999 and 2015. We find that the loss of 1000 trade-related jobs was associated with a 2.7 percent increase in opioid-related deaths. When fentanyl was present in the heroin supply, the same number of job losses was associated with a 11.3 percent increase in opioid-related deaths."

--- I'm very skeptical about OLS here.  Something like nearest neighbors would be better here, but I'm not sure how to handle spatial correlation.]]></description>
<dc:subject>to:NB to_read drugs whats_gone_wrong_with_america class_struggles_in_america econometrics statistics globalization to_teach:data_over_space_and_time to_teach:undergrad-ADA causal_inference</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:56271b63f2a4/</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:drugs"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:whats_gone_wrong_with_america"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:class_struggles_in_america"/>
	<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:globalization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.researchgate.net/publication/333571096_The_Standard_Errors_of_Persistence">
    <title>The Standard Errors of Persistence</title>
    <dc:date>2019-07-17T13:38:45+00:00</dc:date>
    <link>https://www.researchgate.net/publication/333571096_The_Standard_Errors_of_Persistence</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["A large literature on persistence finds that many modern outcomes strongly reflect characteristics of the same places in the distant past. However, alongside unusually high t statistics, these regressions display severe spatial auto-correlation in residuals, and the purpose of this paper is to examine whether these two properties might be connected. We start by running artificial regressions where both variables are spatial noise and find that, even for modest ranges of spatial correlation between points, t statistics become severely inflated leading to significance levels that are in error by several orders of magnitude. We analyse 27 persistence studies in leading journals and find that in most cases if we replace the main explanatory variable with spatial noise the fit of the regression commonly improves; and if we replace the dependent variable with spatial noise, the persistence variable can still explain it at high significance levels. We can predict in advance which persistence results might be the outcome of fitting spatial noise from the degree of spatial au-tocorrelation in their residuals measured by a standard Moran statistic. Our findings suggest that the results of persistence studies, and of spatial regressions more generally, might be treated with some caution in the absence of reported Moran statistics and noise simulations."]]></description>
<dc:subject>econometrics regression spatial_statistics to_teach:data_over_space_and_time via:jbdelong in_NB have_read to_teach:linear_models</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:a2dddfc99614/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
	<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:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:linear_models"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080217-053214">
    <title>Macroeconomic Nowcasting and Forecasting with Big Data | Annual Review of Economics</title>
    <dc:date>2019-05-26T16:39:34+00:00</dc:date>
    <link>https://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080217-053214</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Data, data, data…. Economists know their importance well, especially when it comes to monitoring macroeconomic conditions—the basis for making informed economic and policy decisions. Handling large and complex data sets was a challenge that macroeconomists engaged in real-time analysis faced long before so-called big data became pervasive in other disciplines. We review how methods for tracking economic conditions using big data have evolved over time and explain how econometric techniques have advanced to mimic and automate best practices of forecasters on trading desks, at central banks, and in other market-monitoring roles. We present in detail the methodology underlying the New York Fed Staff Nowcast, which employs these innovative techniques to produce early estimates of GDP growth, synthesizing a wide range of macroeconomic data as they become available."]]></description>
<dc:subject>to:NB economics econometrics macroeconomics social_measurement statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:89337a521d83/</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:econometrics"/>
	<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:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www-jstor-org.proxy.library.cmu.edu/stable/2999545?seq=2#metadata_info_tab_contents">
    <title>Nonparametric Estimation of Triangular Simultaneous Equations Models on JSTOR</title>
    <dc:date>2019-04-24T22:39:57+00:00</dc:date>
    <link>https://www-jstor-org.proxy.library.cmu.edu/stable/2999545?seq=2#metadata_info_tab_contents</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper presents a simple two-step nonparametric estimator for a triangular simultaneous equation model. Our approach employs series approximations that exploit the additive structure of the model. The first step comprises the nonparametric estimation of the reduced form and the corresponding residuals. The second step is the estimation of the primary equation via nonparametric regression with the reduced form residuals included as a regressor. We derive consistency and asymptotic normality results for our estimator, including optimal convergence rates. Finally we present an empirical example, based on the relationship between the hourly wage rate and annual hours worked, which illustrates the utility of our approach."]]></description>
<dc:subject>nonparametrics instrumental_variables causal_inference statistics regression econometrics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:46bcb9e6ddc2/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.103.3.550">
    <title>Whitney K. Newey, &quot;Nonparametric Instrumental Variables Estimation&quot; (2013)</title>
    <dc:date>2019-04-24T22:39:10+00:00</dc:date>
    <link>https://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.103.3.550</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["n many economic models, objects of interest are functions which satisfy conditional moment restrictions. Economics does not restrict the functional form of these models, motivating nonparametric methods. In this paper we review identification results and describe a simple nonparametric instrumental variables (NPIV) estimator. We also consider a simple method of inference. In addition we show how the ability to uncover nonlinearities with conditional moment restrictions is related to the strength of the instruments. We point to applications where important nonlinearities can be found with NPIV and applications where they cannot."]]></description>
<dc:subject>nonparametrics instrumental_variables regression causal_inference statistics econometrics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:30029a2150f1/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA8662">
    <title>Applied Nonparametric Instrumental Variables Estimation - Horowitz - 2011 - Econometrica - Wiley Online Library</title>
    <dc:date>2019-04-24T22:37:30+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/pdf/10.3982/ECTA8662</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used to identify and estimate these parameters. However, linear and other finite‐dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators."]]></description>
<dc:subject>nonparametrics instrumental_variables causal_inference econometrics statistics inverse_problems re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:60ce2bd00280/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<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:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA6539">
    <title>Nonparametric Instrumental Regression - Darolles - 2011 - Econometrica - Wiley Online Library</title>
    <dc:date>2019-04-24T22:35:55+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/abs/10.3982/ECTA6539</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The focus of this paper is the nonparametric estimation of an instrumental regression function ϕ defined by conditional moment restrictions that stem from a structural econometric model E[Y−ϕ(Z)|W]=0, and involve endogenous variables Y and Z and instruments W. The function ϕ is the solution of an ill‐posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyzes identification and overidentification of this model, and presents asymptotic properties of the estimated nonparametric instrumental regression function."]]></description>
<dc:subject>nonparametrics instrumental_variables causal_inference statistics inverse_problems regression econometrics re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:18b720537b4e/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-0262.00459">
    <title>Instrumental Variable Estimation of Nonparametric Models - Newey - 2003 - Econometrica - Wiley Online Library</title>
    <dc:date>2019-04-24T22:34:56+00:00</dc:date>
    <link>https://onlinelibrary.wiley.com/doi/pdf/10.1111/1468-0262.00459</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In econometrics there are many occasions where knowledge of the structural relationship among dependent variables is required to answer questions of interest. This paper gives identification and estimation results for nonparametric conditional moment restrictions. We characterize identification of structural functions as completeness of certain conditional distributions, and give sufficient identification conditions for exponential families and discrete variables. We also give a consistent, nonparametric estimator of the structural function. The estimator is nonparametric two‐stage least squares based on series approximation, which overcomes an ill‐posed inverse problem by placing bounds on integrals of higher‐order derivatives."

]]></description>
<dc:subject>instrumental_variables nonparametrics regression causal_inference statistics econometrics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6b8664a72e96/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://ideas.repec.org/p/mtl/montde/2002-05.html">
    <title>Nonparametric Instrumental Regression</title>
    <dc:date>2019-04-22T16:53:09+00:00</dc:date>
    <link>https://ideas.repec.org/p/mtl/montde/2002-05.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The focus of the paper is the nonparametric estimation of an instrumental regression function P defined by conditional moment restrictions stemming from a structural econometric model : E[Y-P(Z)|W]=0 and involving endogenous variables Y and Z and instruments W. The function P is the solution of an ill-posed inverse problem and we propose an estimation procedure based on Tikhonov regularization. The paper analyses identification and overidentification of this model and presents asymptotic properties of the estimated nonparametric instrumental regression function."

--- Was this ever published?  It definitely seems like the most elegant approach to nonparametric IVs I've seen (French econometricians!).

--- ETA: Yes, in _Econometrica_! [https://doi.org/10.3982/ECTA6539]]]></description>
<dc:subject>have_read regression instrumental_variables nonparametrics inverse_problems causal_inference re:ADAfaEPoV econometrics in_NB statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:69c772c89634/</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:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inverse_problems"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.cambridge.org/core/books/an-introduction-to-the-advanced-theory-and-practice-of-nonparametric-econometrics/974161A820CE022349B95AF2320C25FA">
    <title>An Introduction to the Advanced Theory and Practice of Nonparametric Econometrics by Jeffrey S. Racine</title>
    <dc:date>2019-01-06T02:05:38+00:00</dc:date>
    <link>https://www.cambridge.org/core/books/an-introduction-to-the-advanced-theory-and-practice-of-nonparametric-econometrics/974161A820CE022349B95AF2320C25FA</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Interest in nonparametric methodology has grown considerably over the past few decades, stemming in part from vast improvements in computer hardware and the availability of new software that allows practitioners to take full advantage of these numerically intensive methods. This book is written for advanced undergraduate students, intermediate graduate students, and faculty, and provides a complete teaching and learning course at a more accessible level of theoretical rigor than Racine's earlier book co-authored with Qi Li, Nonparametric Econometrics: Theory and Practice (2007). The open source R platform for statistical computing and graphics is used throughout in conjunction with the R package np. Recent developments in reproducible research is emphasized throughout with appendices devoted to helping the reader get up to speed with R, R Markdown, TeX and Git."

--- Ooh.]]></description>
<dc:subject>to:NB books:noted econometrics nonparametrics statistics racine.jeffrey R coveted</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:85a96986fe7c/</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:nonparametrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:racine.jeffrey"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:R"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:coveted"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.jstor.org/stable/1403345?seq=1#metadata_info_tab_contents">
    <title>A Perspective on the Accuracy of Economic Observations on JSTOR</title>
    <dc:date>2018-10-26T20:46:05+00:00</dc:date>
    <link>https://www.jstor.org/stable/1403345?seq=1#metadata_info_tab_contents</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In 1950 appeared the first edition of Oskar Morgenstern's famous book, The Accuracy of Economic Observations. Nearly half a century later it is timely to return to Morgenstern's diagnosis and to contemplate his therapeutic recommendations. Morgenstern's vision can and should inform the consideration of the topic today because of the continued validity of many of his findings. His work still provides stimuli for studying the general problems of measurement, the varying requirements for accuracy, the issues of aggregate macroeconomic measures, and the prospects for economic and social measurement. This is so even if some of the bleaker assessments by Morgenstern, notwithstanding their technical merits, provide little or no practical guidance for statistical activities. In this context it is enlightening to recall the different practical attitudes adopted by Keynes and by some of his contemporaries in Germany regarding theoretical difficulties with aggregate macroeconomic data."]]></description>
<dc:subject>to:NB to_read social_measurement social_science_methodology economics econometrics on_the_accuracy_of_economic_observations</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1fc6d4ca3c32/</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:social_measurement"/>
	<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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:on_the_accuracy_of_economic_observations"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jep.15.4.143">
    <title>Quantile Regression</title>
    <dc:date>2018-10-24T14:19:54+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jep.15.4.143</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of classical least squares estimation of conditional mean models to the estimation of an ensemble of models for several conditional quantile functions. The central special case is the median regression estimator which minimizes a sum of absolute errors. Other conditional quantile functions are estimated by minimizing an asymmetrically weighted sum of absolute errors. Quantile regression methods are illustrated with applications to models for CEO pay, food expenditure, and infant birthweight."]]></description>
<dc:subject>to:NB have_read regression statistics econometrics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:67e7d8d009d7/</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:have_read"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jel.53.3.631">
    <title>Communicating Uncertainty in Official Economic Statistics: An Appraisal Fifty Years after Morgenstern</title>
    <dc:date>2018-10-22T13:19:18+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jel.53.3.631</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Federal statistical agencies in the United States and analogous agencies elsewhere commonly report official economic statistics as point estimates, without accompanying measures of error. Users of the statistics may incorrectly view them as error free or may incorrectly conjecture error magnitudes. This paper discusses strategies to mitigate misinterpretation of official statistics by communicating uncertainty to the public. Sampling error can be measured using established statistical principles. The challenge is to satisfactorily measure the various forms of nonsampling error. I find it useful to distinguish transitory statistical uncertainty, permanent statistical uncertainty, and conceptual uncertainty. I illustrate how each arises as the Bureau of Economic Analysis periodically revises GDP estimates, the Census Bureau generates household income statistics from surveys with nonresponse, and the Bureau of Labor Statistics seasonally adjusts employment statistics. I anchor my discussion of communication of uncertainty in the contribution of Oskar Morgenstern (1963a), who argued forcefully for agency publication of error estimates for official economic statistics."]]></description>
<dc:subject>to:NB to_read statistics econometrics via:arsyed manski.charles_f.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:81cf90a2d0b7/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:arsyed"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:manski.charles_f."/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://hal.archives-ouvertes.fr/hal-01841413">
    <title>Archive ouverte HAL - The Great Regression. Machine Learning, Econometrics, and the Future of Quantitative Social Sciences</title>
    <dc:date>2018-08-06T14:58:40+00:00</dc:date>
    <link>https://hal.archives-ouvertes.fr/hal-01841413</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["What can machine learning do for (social) scientific analysis, and what can it do to it? A contribution to the emerging debate on the role of machine learning for the social sciences, this article offers an introduction to this class of statistical techniques. It details its premises, logic, and the challenges it faces. This is done by comparing machine learning to more classical approaches to quantification – most notably parametric regression– both at a general level and in practice. The article is thus an intervention in the contentious debates about the role and possible consequences of adopting statistical learning in science. We claim that the revolution announced by many and feared by others will not happen any time soon, at least not in the terms that both proponents and critics of the technique have spelled out. The growing use of machine learning is not so much ushering in a radically new quantitative era as it is fostering an increased competition between the newly termed classic method and the learning approach. This, in turn, results in more uncertainty with respect to quantified results. Surprisingly enough, this may be good news for knowledge overall."

--- The correct line here is that 90%+ of "machine learning" is rebranded non-parametric regression, which is what the social sciences should have been doing all along anyway, because they have no good theories which suggest particular parametric forms.  (Partial exceptions: demography and epidemiology.)  If the resulting confidence sets are bigger than they'd like, that's still the actual range of uncertainty they need to live with, until they can reduce it with more and better empirical information, or additional constraints from well-supported theories.  (Arguably, this was all in Haavelmo.)  I look forward to seeing whether this paper grasps these obvious truths.]]></description>
<dc:subject>to:NB to_read regression social_science_methodology machine_learning via:phnk econometrics color_me_skeptical</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:b09254b30dd6/</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:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:machine_learning"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:phnk"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:color_me_skeptical"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://www.aeaweb.org/articles?id=10.1257/jel.20160854">
    <title>Identifying and Estimation Neighborhood Effects</title>
    <dc:date>2018-06-14T21:11:59+00:00</dc:date>
    <link>https://www.aeaweb.org/articles?id=10.1257/jel.20160854</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Residential segregation by race and income are enduring features of urban America. Understanding the effects of residential segregation on educational attainment, labor market outcomes, criminal activity, and other outcomes has been a leading project of the social sciences for over half a century. This paper describes techniques for measuring the effects of neighborhood of residence on long-run life outcomes."

--- Last tag very tentative, since I've not read the paper _and_ the class won't be that advanced]]></description>
<dc:subject>to:NB statistics econometrics spatial_statistics identifiability inequality the_american_dilemma to_teach:data_over_space_and_time</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cb4a312f2fab/</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:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:spatial_statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:identifiability"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:inequality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:the_american_dilemma"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:data_over_space_and_time"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://jwmason.org/slackwire/the-wit-and-wisdom-of-trygve-haavelmo/">
    <title>The Wit and Wisdom of Trygve Haavelmo – J. W. Mason</title>
    <dc:date>2018-05-30T15:40:54+00:00</dc:date>
    <link>http://jwmason.org/slackwire/the-wit-and-wisdom-of-trygve-haavelmo/</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>economics econometrics social_science_methodology haavelmo.trygve</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:f2d6cdccee1b/</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:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:haavelmo.trygve"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://personal.lse.ac.uk/YoungA/CWOI.pdf">
    <title>Consistency without Inference: Instrumental Variables in Practical Application</title>
    <dc:date>2017-11-14T15:30:41+00:00</dc:date>
    <link>https://personal.lse.ac.uk/YoungA/CWOI.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I use the bootstrap to study a comprehensive sample of 1400 instrumental
variables regressions in 32 papers published in the journals of the American
Economic Association. IV estimates are more often found to be falsely significant
and more sensitive to outliers than OLS, while having a higher mean squared error
around the IV population moment. There is little evidence that OLS estimates are
substantively biased, while IV instruments often appear to be irrelevant. In
addition, I find that established weak instrument pre-tests are largely
uninformative and weak instrument robust methods generally perform no better or
substantially worse than 2SLS. "]]></description>
<dc:subject>re:ADAfaEPoV to_teach:undergrad-ADA instrumental_variables causal_inference regression statistics econometrics via:kjhealy have_read in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:0762e8318a2c/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:to_teach:undergrad-ADA"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:kjhealy"/>
	<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://press.princeton.edu/titles/11025.html">
    <title>Imai, K.: Quantitative Social Science: An Introduction. (eBook, Paperback and Hardcover)</title>
    <dc:date>2017-06-23T16:48:34+00:00</dc:date>
    <link>http://press.princeton.edu/titles/11025.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it—or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science.
"Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results—it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior.
"Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors.
"Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science
"Provides hands-on instruction using R programming, not paper-and-pencil statistics
"Includes more than forty data sets from actual research for students to test their skills on
"Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools
"Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises
"Offers a solid foundation for further study
"Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides"]]></description>
<dc:subject>to:NB books:noted social_science_methodology economics statistics econometrics causal_inference re:ADAfaEPoV</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:71914f0fbd67/</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:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:re:ADAfaEPoV"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://sss.sagepub.com/content/46/5/701.abstract">
    <title>Econometrics as evidence? Examining the ‘causal’ connections between financial speculation and commodities prices</title>
    <dc:date>2016-10-18T20:57:07+00:00</dc:date>
    <link>http://sss.sagepub.com/content/46/5/701.abstract</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["One of the lasting legacies of the financial crisis of 2008, and the legislative energies that followed from it, is the growing reliance on econometrics as part of the rulemaking process. Financial regulators are increasingly expected to rationalize proposed rules using available econometric techniques, and the courts have vacated several key rules emanating from Dodd-Frank on the grounds of alleged deficiencies in this evidentiary effort. The turn toward such econometric tools is seen as a significant constraint on and challenge to regulators as they endeavor to engage with such essential policy questions as the impact of financial speculation on food security. Yet, outside of the specialized practitioner community, very little is known about these techniques. This article examines one such econometric test, Granger causality, and its role in a pivotal Dodd-Frank rulemaking. Through an examination of the test for Granger causality and its attempts to distill the causal connections between financial speculation and commodities prices, the article argues that econometrics is a blunt but useful tool, limited in its ability to provide decisive insights into commodities markets and yet yielding useful returns for those who are able to wield it."]]></description>
<dc:subject>to:NB sociology_of_science econometrics financial_speculation regulation causal_inference time_series statistics granger_causality</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:17ff7d387123/</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:sociology_of_science"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:financial_speculation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regulation"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<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:granger_causality"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://press.princeton.edu/titles/7837.html">
    <title>Greiner, A. and Semmler, W., Gong, G.: The Forces of Economic Growth: A Time Series Perspective. (eBook, Paperback and Hardcover)</title>
    <dc:date>2016-07-08T21:02:17+00:00</dc:date>
    <link>http://press.princeton.edu/titles/7837.html</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["In economics, the emergence of New Growth Theory in recent decades has directed attention to an old and important problem: what are the forces of economic growth and how can public policy enhance them? This book examines major forces of growth--including spillover effects and externalities, education and formation of human capital, knowledge creation through deliberate research efforts, and public infrastructure investment. Unique in emphasizing the importance of different forces for particular stages of development, it offers wide-ranging policy implications in the process.
"The authors critically examine recently developed endogenous growth models, study the dynamic implications of modified models, and test the models empirically with modern time series methods that avoid the perils of heterogeneity in cross-country studies. Their empirical analyses, undertaken with newly constructed time series data for the United States and some core countries of the Euro zone, show that models containing scale effects, such as the R&D model and the human capital model, are compatible with time series evidence only after considerable modifications and nonlinearities are introduced. They also explore the relationship between growth and inequality, with particular focus on technological change and income disparity. The Forces of Economic Growth represents a comprehensive and up-to-date empirical time series perspective on the New Growth Theory."]]></description>
<dc:subject>to:NB books:noted economics economic_growth econometrics time_series statistics</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:636515e07758/</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:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_growth"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:time_series"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="https://doi.org/10.1371/journal.pone.0152719">
    <title>Statistically controlling for confounding constructs is harder than you think</title>
    <dc:date>2016-03-03T13:46:30+00:00</dc:date>
    <link>https://doi.org/10.1371/journal.pone.0152719</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Social scientists often seek to demonstrate that a construct has incremental validity over and above other related constructs. However, these claims are typically supported by measurement- level models that fail to consider the effects of measurement (un)reliability. We use intuitive examples, Monte Carlo simulations, and a novel analytical framework to demonstrate that common strategies for establishing incremental construct validity using multiple regression analysis exhibit extremely high Type I error rates under parameter regimes common in many psychological domains. Counterintuitively, we find that error rates are highest—in some cases approaching 100%—when sample sizes are large and reliability is moderate. Our findings suggest that a potentially large proportion of incremental validity claims made in the literature are spurious. We present a web application (http://jakewestfall.org/ivy/) that readers can use to explore the statistical properties of these and other incremental validity arguments. We conclude by reviewing SEM-based statistical approaches that appropriately control the Type I error rate when attempting to establish incremental validity."]]></description>
<dc:subject>have_read measurement social_measurement social_science_methodology psychometrics econometrics graphical_models statistics to_teach:undergrad-ADA re:ADAfaEPoV yarkoni.tal to:blog in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:cc7dbb45aecc/</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:measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_measurement"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:social_science_methodology"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:psychometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<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:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:yarkoni.tal"/>
	<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://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://fivethirtyeight.com/features/the-science-of-grading-teachers-gets-high-marks/">
    <title>The Science Of Grading Teachers Gets High Marks | FiveThirtyEight</title>
    <dc:date>2015-09-01T23:33:35+00:00</dc:date>
    <link>http://fivethirtyeight.com/features/the-science-of-grading-teachers-gets-high-marks/</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA[Errr, if there's a _systematic_ flaw in the research design, which is what the critics are saying, then it's not surprising that applying the same design to a different data set gives similar results!  (That's pretty much what it means for the flaw to be systematic.)]]></description>
<dc:subject>track_down_references value-added_measurement_in_education econometrics via:jbdelong have_read</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:7528904d0471/</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:value-added_measurement_in_education"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jbdelong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1507.06115">
    <title>[1507.06115] Generalized Indirect Inference for Discrete Choice Models</title>
    <dc:date>2015-08-05T15:01:14+00:00</dc:date>
    <link>http://arxiv.org/abs/1507.06115</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["This paper develops and implements a practical simulation-based method for estimating dynamic discrete choice models. The method, which can accommodate lagged dependent variables, serially correlated errors, unobserved variables, and many alternatives, builds on the ideas of indirect inference. The main difficulty in implementing indirect inference in discrete choice models is that the objective surface is a step function, rendering gradient-based optimization methods useless. To overcome this obstacle, this paper shows how to smooth the objective surface. The key idea is to use a smoothed function of the latent utilities as the dependent variable in the auxiliary model. As the smoothing parameter goes to zero, this function delivers the discrete choice implied by the latent utilities, thereby guaranteeing consistency. We establish conditions on the smoothing such that our estimator enjoys the same limiting distribution as the indirect inference estimator, while at the same time ensuring that the smoothing facilitates the convergence of gradient-based optimization methods. A set of Monte Carlo experiments shows that the method is fast, robust, and nearly as efficient as maximum likelihood when the auxiliary model is sufficiently rich."]]></description>
<dc:subject>indirect_inference optimization statistics econometrics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:6c00fd7aef87/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:indirect_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:optimization"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp14-19.pdf?uol_r=d307e306">
    <title>On the Interpretation of Instrumental Variables in the Presence of Specification Errors</title>
    <dc:date>2015-02-17T17:23:40+00:00</dc:date>
    <link>http://www.le.ac.uk/economics/research/RePEc/lec/leecon/dp14-19.pdf?uol_r=d307e306</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The method of instrumental variables (IV) and the generalized method of moments (GMM), and their applications to the estimation of errors-in-variables and simultaneous equations models in econometrics, require data on a sufficient number of instrumental variables that are both exogenous and relevant. We argue that, in general, such instruments (weak or strong) cannot exist."

--- I think they are too quick to dismiss non-parametric IV; if what one wants is consistent estimates of the partial derivatives at a given point, you _can_ get that by (e.g.) splines or locally linear regression.  Need to think through this in terms of Pearl's graphical definition of IVs.]]></description>
<dc:subject>instrumental_variables misspecification regression linear_regression causal_inference statistics econometrics via:jbdelong have_read to_teach:undergrad-ADA re:ADAfaEPoV in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:5e670302d3ca/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:misspecification"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:linear_regression"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:via:jbdelong"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:have_read"/>
	<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:ADAfaEPoV"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.0163">
    <title>[1410.0163] Instrumental Variables: An Econometrician's Perspective</title>
    <dc:date>2015-01-20T02:39:49+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.0163</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["I review recent work in the statistics literature on instrumental variables methods from an econometrics perspective. I discuss some of the older, economic, applications including supply and demand models and relate them to the recent applications in settings of randomized experiments with noncompliance. I discuss the assumptions underlying instrumental variables methods and in what settings these may be plausible. By providing context to the current applications, a better understanding of the applicability of these methods may arise."]]></description>
<dc:subject>econometrics economics instrumental_variables causal_inference statistics in_NB</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:3e76f63303fa/</dc:identifier>
<taxo:topics><rdf:Bag>	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:causal_inference"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:in_NB"/>
</rdf:Bag></taxo:topics>
</item>
<item rdf:about="http://arxiv.org/abs/1410.0470">
    <title>[1410.0470] ACE Bounds; SEMs with Equilibrium Conditions</title>
    <dc:date>2015-01-20T01:56:09+00:00</dc:date>
    <link>http://arxiv.org/abs/1410.0470</link>
    <dc:creator>cshalizi</dc:creator><dc:subject>to:NB causal_inference causality graphical_models statistics economics econometrics richardson.thomas robins.james_m.</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:87a7b302af00/</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:causality"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:graphical_models"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:statistics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:richardson.thomas"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:robins.james_m."/>
</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.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"/>
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<item rdf:about="http://mitpress.mit.edu/books/empirical-model-discovery-and-theory-evaluation">
    <title>Empirical Model Discovery and Theory Evaluation: Automatic Selection Methods in Econometrics | The MIT Press</title>
    <dc:date>2014-11-21T16:44:13+00:00</dc:date>
    <link>http://mitpress.mit.edu/books/empirical-model-discovery-and-theory-evaluation</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["Economic models of empirical phenomena are developed for a variety of reasons, the most obvious of which is the numerical characterization of available evidence, in a suitably parsimonious form. Another is to test a theory, or evaluate it against the evidence; still another is to forecast future outcomes. Building such models involves a multitude of decisions, and the large number of features that need to be taken into account can overwhelm the researcher. Automatic model selection, which draws on recent advances in computation and search algorithms, can create, and then empirically investigate, a vastly wider range of possibilities than even the greatest expert. In this book, leading econometricians David Hendry and Jurgen Doornik report on their several decades of innovative research on automatic model selection.
"After introducing the principles of empirical model discovery and the role of model selection, Hendry and Doornik outline the stages of developing a viable model of a complicated evolving process. They discuss the discovery stages in detail, considering both the theory of model selection and the performance of several algorithms. They describe extensions to tackling outliers and multiple breaks, leading to the general case of more candidate variables than observations. Finally, they briefly consider selecting models specifically for forecasting."]]></description>
<dc:subject>books:noted econometrics statistics model_selection model_discovery in_NB books:owned</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:79bb2df096fe/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_selection"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:model_discovery"/>
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<item rdf:about="https://www.aeaweb.org/articles.php?doi=10.1257/aer.104.11.3635">
    <title>AER (104,11) p. 3635 - Structural Transformation, the Mismeasurement of Productivity Growth, and the Cost Disease of Services</title>
    <dc:date>2014-10-27T19:21:12+00:00</dc:date>
    <link>https://www.aeaweb.org/articles.php?doi=10.1257/aer.104.11.3635</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["If workers self-select into industries based upon their relative productivity in different tasks, and comparative advantage is aligned with absolute advantage, then the average efficacy of a sector's workforce will be negatively correlated with its employment share. This might explain the difference in the reported productivity growth of contracting goods and expanding services. Instrumenting with defense expenditures, I find the elasticity of worker efficacy with respect to employment shares is substantially negative, albeit imprecisely estimated. The estimates suggest that the view that goods and services have similar productivity growth rates is a plausible alternative characterization of growth in developed economies."

--- How on Earth is that a valid instrument for this question???]]></description>
<dc:subject>to:NB to_read economics economic_growth productivity econometrics instrumental_variables</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:fc3ffaa10995/</dc:identifier>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:economic_growth"/>
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	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:econometrics"/>
	<rdf:li rdf:resource="https://pinboard.in/u:cshalizi/t:instrumental_variables"/>
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</item>
<item rdf:about="http://public.econ.duke.edu/~kdh9/Source%20Materials/Research/econometric_methodology_plus_abstract.pdf">
    <title>Methodology of Econometrics</title>
    <dc:date>2014-09-24T21:54:51+00:00</dc:date>
    <link>http://public.econ.duke.edu/~kdh9/Source%20Materials/Research/econometric_methodology_plus_abstract.pdf</link>
    <dc:creator>cshalizi</dc:creator><description><![CDATA["The methodology of econometrics is not the study of particular econometric techniques, but a meta-study of how econometrics contributes to economic science. As such it is part of the philosophy of science. The essay begins by reviewing the salient points of the main approaches to the philosophy of science – particularly, logical positivism, Popper’s falsificationism, Lakatos methodology of scientific research programs, and the semantic approach – and orients econometrics within them. The principal methodological issues for econometrics are the application of probability theory to economics and the mapping between economic theory and probability models. Both are raised in Haavelmo’s (1944) seminal essay. Using that essay as a touchstone, the various recent approaches to econometrics are surveyed – those of the Cowles Commission, the vector autoregression program, the LSE approach, calibration, and a set of common, but heterogeneous approaches encapsulated as the “textbook econometrics.” Finally, the essay considers the light shed by econometric methodology on the main epistemological and ontological questions raised in the philosophy of science."]]></description>
<dc:subject>to:NB econometrics economics statistics philosophy_of_science</dc:subject>
<dc:source>https://pinboard.in/</dc:source>
<dc:identifier>https://pinboard.in/u:cshalizi/b:1e7c7fe56a95/</dc:identifier>
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