【教材分享】Mostly Harmless Econometrics: An Empiricist's Companion

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教材信息：
名字：Mostly Harmless Econometrics: An Empiricist's Companion
作者：Joshua D. Angrist, Jörn-Steffen Pischke
出版商: Princeton University Press
大致内容：
I Introduction 1
1 Questions about Questions 3

2 The Experimental Ideal 9
2.1 The Selection Problem........................................ 10
2.2 Random Assignment Solvesthe Selection Problem ........................ 12
2.3 Regression Analysis of Experiments................................. 16

II The Core 19
3 Making Regression Make Sense 21
3.1 RegressionFundamentals ...................................... 22
3.1.1 Economic Relationships and the Conditional Expectation Function . . . . . . . . . . . 23
3.1.2 Linear Regression and the CEF............................... 26
3.1.3 Asymptotic OLS Inference.................................. 30
3.1.4 Saturated Models, Main Effects, and Other Regression Talk . . . . . . . . . . . . . . . 36
3.2 Regression and Causality ...................................... 38
3.2.1 The Conditional Independence Assumption ........................ 38
3.2.2 The Omitted Variables Bias Formula............................ 44
3.2.3 Bad Control.......................................... 47
3.3 Heterogeneity and Nonlinearity ................................... 51
3.3.1 Regression Meets Matching ................................... 51
3.3.2 Control for Covariates Using the Propensity Score . . . . . . . . . . . . . . . . . . . . 59
3.3.3 Propensity-Score Methods vs. Regression ......................... 6
3.4 Regression Details .......................................... 66
3.4.1 Weighting Regression .................................... 66
3.4.2 Limited Dependent Variables and Marginal Effects . . . . . . . . . . . . . . . . . . . . 69
3.4.3 Why is Regression Called Regression and What Does Regression-to-the-mean Mean? . 80

4 Instrumental Variables in Action: Sometimes You Get What You Need 83
4.1 IV and causality ........................................... 84
4.1.1 Two-Stage Least Squares .................................. 89
4.1.2 The Wald Estimator..................................... 94
4.1.3 Grouped Data and 2SLS................................... 100
4.2 Asymptotic 2SLS Inference ..................................... 103
4.2.1 The Limiting Distribution of the 2SLS Coefficient Vector . . . . . . . . . . . . . . . . 103
4.2.2 Over-identification and the 2SLS Minimand ....................... 105
4.3 Two-Sample IV and Split-Sample IV ............................... 109
4.4 IV with Heterogeneous Potential Outcomes ............................ 111
4.4.1 Local Average Treatment Effects .............................. 112
4.4.2 The Compliant Subpopulation ............................... 117
4.4.3 IV in Randomized Trials................................... 119
4.4.4 Counting and Characterizing Compliers .......................... 123
4.5 Generalizing LATE..........................................130
4.5.1 LATE with Multiple Instruments.............................. 130
4.5.2 Covariates in the Heterogeneous-effects Model....................... 131
4.5.3 Average Causal Response with Variable Treatment Intensity . . . . . . . . . . . . . . 136
4.6 IV Details...............................................141
4.6.1 2SLS Mistakes ........................................141
4.6.2 Peer Effects..........................................144
4.6.3 Limited Dependent Variables Reprise ........................... 147
4.6.4 The Bias of 2SLSF......................................153
4.7 Appendix ...............................................160

5 Parallel Worlds: Fixed Effects, Differences-in-differences, and Panel Data 165
5.1 Individual Fixed Effects.......................................165
5.2 Differences-in-differences.......................................169

5.2.1 Regression DD ........................................174
5.3 Fixed Effects versus Lagged Dependent Variables.........................182
5.4 Appendix: More on fixed effects and lagged dependent variables . . . . . . . . . . . . . . . . 184

III Extensions 187
6 Getting a Little Jumpy: Regression Discontinuity Designs 189
6.1 SharpRD...............................................189
6.2 Fuzzy RD is IV............................................196

7 Quantile Regression 203
7.1 The Quantile Regression Model................................... 204
7.1.1 Censored Quantile Regression................................ 208
7.1.2 The Quantile Regression Approximation Property . . . . . . . . . . . . . . . . . . . 210
7.1.3 Tricky Points.........................................213
7.2 QuantileTreatment Effects ..................................... 214
7.2.1 The QTE Estimator ..................................... 216

8 Nonstandard Standard Error Issues 221
8.1 The Bias of Robust Standard Errors ............................... 222
8.2 Clustering and Serial Correlation in Panels ............................ 231
8.2.1 Clustering and the Moulton Factor............................. 231
8.2.2 Serial Correlation in Panels and Difference-in-Difference Models . . . . . . . . . . . . . 236
8.2.3 Fewer than 42 clusters....................................238
8.3 Appendix:DerivationofthesimpleMoultonfactor........................ 241

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