Mostly Harmless Econometrics

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  • Format: Paperback
  • Copyright: 2008-12-15
  • Publisher: Ingram Pub Services

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Supplemental Materials

What is included with this book?


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

Author Biography

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.

Table of Contents

List of Figuresp. vii
List of Tablesp. ix
Prefacep. xi
Acknowledgmentsp. xv
Organization of This Bookp. xvii
Preliminariesp. 1
Questions about Questionsp. 3
The Experimental Idealp. 11
The Selection Problemp. 12
Random Assignment Solves the Selection Problemp. 15
Regression Analysis of Experimentsp. 22
The Corep. 25
Making Regression Make Sensep. 27
Regression Fundamentalsp. 28
Regression and Causalityp. 51
Heterogeneity and Nonlinearityp. 68
Regression Detailsp. 91
Appendix: Derivation of the Average Derivative Weighting Functionp. 110
Instrumental Variables in Action: Sometimes You Get What You Needp. 113
IV and Causalityp. 115
Asymptotic 2SLS Inferencep. 138
Two-Sample IV and Split-Sample IVp. 147
IV with Heterogeneous Potential Outcomesp. 150
Generalizing LATEp. 173
IV Detailsp. 188
Appendixp. 216
Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Datap. 221
Individual Fixed Effectsp. 221
Differences-in-Differencesp. 227
Fixed Effects versus Lagged Dependent Variablesp. 243
Appendix: More on Fixed Effects and Lagged Dependent Variablesp. 246
Extensionsp. 249
Getting a Little Jumpy: Regression Discontinuity Designsp. 251
Sharp RDp. 251
Fuzzy RD Is IVp. 259
Quantile Regressionp. 269
The Quantile Regression Modelp. 270
IV Estimation of Quantile Treatment Effectsp. 283
Nonstandard Standard Error Issuesp. 293
The Bias of Robust Standard Error Estimatesp. 294
Clustering and Serial Correlation in Panelsp. 308
Appendix: Derivation of the Simple Moulton Factorp. 323
Last Wordsp. 327
Acronyms and Abbreviationsp. 329
Empirical Studies Indexp. 335
Referencesp. 339
Indexp. 361
Table of Contents provided by Ingram. All Rights Reserved.

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