"This pathbreaking book is a must-read for any scientist who is interested in formulating and testing hypotheses about the social world. This includes political scientists, sociologists, historians, geographers, and anthropologists. The book is clever and funny, and guides you through the tangle of problems that confront empirical research in social science. I wish I had had it years ago."--James Robinson, Harvard University
"What a fascinating and useful book! The application of econometrics in empirical research is as much art as science. What is most distinctive about Mostly Harmless Econometrics relative to other graduate-level econometrics books (besides the colorful prose style!) is that because the authors are longtime practitioners of applied microeconometrics, they speak often and insightfully about the art. I expect it's a great thing to work in the same department with Angrist or Pischke and to be able to ask their advice. Having this book close at hand is the next best thing. When you consult the book to see 'What would Angrist and Pischke do?' about econometric issues you encounter in your own research, you won't necessarily end up doing what they would in every single instance, but I bet you always will benefit from getting their take on the issue."--Gary Solon, Michigan State University
"Interesting and unusual, this is an econometrics book with attitude. It offers real answers and suggestions to problems faced daily by those engaged in the analysis of economic data. I will recommend it to my students."--Guido Imbens, Harvard University
"A well-written and very quirky take on econometric practice."--Orley Ashenfelter, Princeton University
Inhaltsverzeichnis
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
