Mostly Harmless Econometrics An Empiricist's Companion
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Description: The core methods in today's econometric toolkit are linear regression for statistical control, instrumental variables methods for the analysis of natural experiments, and differences-in-differences methods that exploit policy changes. In the modern experimentalist paradigm, these techniques address clear causal questions such as: Do smaller classes increase learning? Should wife batterers be arrested? How much does education raise wages?Mostly Harmless Econometricsshows how the basic tools of applied econometrics allow the data to speak. In addition to econometric essentials,Mostly Harmless Econometricscovers important new extensions--regression-discontinuity designs and quantile regression--as well as how to get standard errors right. Joshua Angrist and Jouml;rn-Steffen Pischke explain why fancier econometric techniques are typically unnecessary and even dangerous. The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social science. An irreverent review of econometric essentials A focus on tools that applied researchers use most Chapters on regression-discontinuity designs, quantile regression, and standard errors Many empirical examples A clear and concise resource with wide applications
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All the information you need in one place! Each Study Brief is a summary of one specific subject; facts, figures, and explanations to help you learn faster.
Copyright year: 2008
Publisher: Princeton University Press
Publication date: 1/4/2009
Size: 5.50" wide x 8.25" long x 1.25" tall
Joshua D. Angrist is professor of economics at the Massachusetts Institute of Technology. Jorn-Steffen Pischke is professor of economics at the London School of Economics and Political Science.
|List of Figures|
|List of Tables|
|Organization of This Book|
|Questions about Questions|
|The Experimental Ideal|
|The Selection Problem|
|Random Assignment Solves the Selection Problem|
|Regression Analysis of Experiments|
|Making Regression Make Sense|
|Regression and Causality|
|Heterogeneity and Nonlinearity|
|Appendix: Derivation of the Average Derivative Weighting Function|
|Instrumental Variables in Action: Sometimes You Get What You Need|
|IV and Causality|
|Asymptotic 2SLS Inference|
|Two-Sample IV and Split-Sample IV|
|IV with Heterogeneous Potential Outcomes|
|Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data|
|Individual Fixed Effects|
|Fixed Effects versus Lagged Dependent Variables|
|Appendix: More on Fixed Effects and Lagged Dependent Variables|
|Getting a Little Jumpy: Regression Discontinuity Designs|
|Fuzzy RD Is IV|
|The Quantile Regression Model|
|IV Estimation of Quantile Treatment Effects|
|Nonstandard Standard Error Issues|
|The Bias of Robust Standard Error Estimates|
|Clustering and Serial Correlation in Panels|
|Appendix: Derivation of the Simple Moulton Factor|
|Acronyms and Abbreviations|
|Empirical Studies Index|