Mostly Harmless Econometrics: An Empiricist's Companion
Authors:
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Joshua D. Angrist and Jörn-Steffen Pischke |
| Publisher: |
Princeton University Press |
| Copyright: |
2009 |
| ISBN-13: |
978-0-691-12035-5 |
| Pages: |
392; paperback |
| Price: |
$28.00 |
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Comment from the Stata technical group
All graduate students and researchers should read Mostly Harmless
Econometrics: An Empiricist’s Companion, by Joshua D. Angrist and
Jörn-Steffen Pischke. This instructive and irreverent romp through
microeconometrics is as much of a page turner as we are likely to see in a
book about statistical methods.
Angrist and Pischke provide an excellent introduction to microeconometrics
from the potential-outcomes approach. They advocate using the
potential-outcomes approach as the method to identify and interpret causal
effects, which can be estimated by the methods discussed in the book. The
authors provide many examples of how this approach has been applied in
recent empirical work and closely link these empirical papers to the methods
discussed.
The book covers ordinary least squares, generalized least squares,
instrumental variables, panel-data estimators, regression-discontinuity
methods, quantile regression, and standard-error estimation.
Maximum-likelihood methods are not discussed in detail; in fact, Angrist and
Pischke argue that simple regression methods frequently provide more robust
estimators of the causal effects of interest in cases where
maximum-likelihood methods are traditionally used.
This book will be required reading; methods experts and practitioners are
already discussing its pros and cons. Fortunately, Angrist and Pischke have
made this book as entertaining as it is informative.
Table of contents
List of Figures
List of Tables
Preface
Acknowledgments
Organization of This Book
I Preliminaries
1 Questions about Questions
2 The Experimental Ideal
2.1 The Selection Problem
2.2 Random Assignment Solves the Selection Problem
2.3 Regression Analysis of Experiments
II The Core
3 Making Regression Make Sense
3.1 Regression Fundamentals
3.2 Regression and Causality
3.3 Heterogeneity and Nonlinearity
3.4 Regression Details
3.5 Appendix: Derivation of the Average Derivative Weighting Function
4 Instrumental Variables in Action: Sometimes You Get What
You Need
4.1 IV and Causality
4.2 Asymptotic 2SLS Inference
4.3 Two-Sample IV and Split-Sample IV
4.4 IV with Heterogeneous Potential Outcomes
4.5 Generalizing LATE
4.6 IV Details
4.7 Appendix
5 Parallel Worlds: Fixed Effects,
Differences-in-Differences, and Panel Data
5.1 Individual Fixed Effects
5.2 Differences-in-Differences
5.3 Fixed Effects versus Lagged Dependent Variables
5.4 Appendix: More on Fixed Effects and Lagged Dependent Variables
III Extensions
6 Getting a Little Jumpy: Regression Discontinuity Designs
6.1 Sharp RD
6.2 Fuzzy RD Is IV
7 Quantile Regression
7.1 The Quantile Regression Model
7.2 IV Estimation of Quantile Treatment Effects
8 Nonstandard Standard Error Issues
8.1 The Bias of Robust Standard Error Estimates
8.2 Clustering and Serial Correlation in Panels
8.3 Appendix: Derivation of the Simple Moulton Factor
Last Words
Acronyms and Abbreviations
Empirical Studies Index
References
Index
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