Mostly Harmless Econometrics

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.

"A quirky and thought-provoking read for any budding econometrician... Insightful and refreshing."--James Davidson, Times Higher Education "I'd recommend it to the entire range of empirical economists, from those still in training to those who, like me, have only a hazy memory of statistical theory and stick to our tried and tested methods of estimation ... an excellent guide to how to do basic regression/IV/panel data estimation really well. In particular, it demonstrates through many examples how to bring about a happy marriage between one's underlying model and the data which might or might not confirm the researcher's hypotheses."--Diane Coyle, The Enlightened Economist Blog "The applied econometric methods emphasized in this book are easy to use and relevant for many areas of contemporary social sciences."--Pavel Stoynov, Zentralblatt MATH "[T]he matter covered in the book is surely of interest to most agricultural economists. Even if it is not a complete overview of existing econometric research methods, it certainly contains a good deal of hands on advice driven by years of experience."--European Review of Agricultural Economics "This book is an extremely thought-provoking contribution to the literature. It champions a different paradigm to that characterising most econometrics texts and does so with considerable (idiosyncratic) style and grace. Highly recommended!"--David Harris and Christopher L. Skeels, Economic Record

• List of Figures vii List of Tables ix Preface xi Acknowledgments xv Organization of This Book xvii PART I: PRELIMINARIES 1 Chapter 1: Questions about Questions 3 Chapter 2: The Experimental Ideal 11 2.1 The Selection Problem 12 2.2 Random Assignment Solves the Selection Problem 15 2.3 Regression Analysis of Experiments 22 PART II: THE CORE 25 Chapter 3: Making Regression Make Sense 27 3.1 Regression Fundamentals 28 3.2 Regression and Causality 51 3.3 Heterogeneity and Nonlinearity 68 3.4 Regression Details 91 3.5 Appendix: Derivation of the Average Derivative Weighting Function 110 Chapter 4: Instrumental Variables in Action: Sometimes You Get What You Need 113 4.1 IV and Causality 115 4.2 Asymptotic 2SLS Inference 138 4.3 Two-Sample IV and Split-Sample IV 147 4.4 IV with Heterogeneous Potential Outcomes 150 4.5 Generalizing LATE 173 4.6 IV Details 188 4.7 Appendix 216 Chapter 5: Parallel Worlds: Fixed Effects, Differences-in-Differences, and Panel Data 221 5.1 Individual Fixed Effects 221 5.2 Differences-in-Differences 227 5.3 Fixed Effects versus Lagged Dependent Variables 243 5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables 246 PART III: EXTENSIONS 249 Chapter 6: Getting a Little Jumpy: Regression Discontinuity Designs 251 6.1 Sharp RD 251 6.2 Fuzzy RD Is IV 259 Chapter 7: Quantile Regression 269 7.1 The Quantile Regression Model 270 7.2 IV Estimation of Quantile Treatment Effects 283 Chapter 8: Nonstandard Standard Error Issues 293 8.1 The Bias of Robust Standard Error Estimates 294 8.2 Clustering and Serial Correlation in Panels 308 8.3 Appendix: Derivation of the Simple Moulton Factor 323 Last Words 327 Acronyms and Abbreviations 329 Empirical Studies Index 335 References 339 Index 361