Econometric analysis of panel data

Written by one of the world's leading researchers and writers in the field , Econometric Analysis of Panel Data has become established as the leading textbook for postgraduate courses in panel data. This new edition reflects the rapid developments in the field covering the vast research that has been conducted on panel data since its initial publication. Featuring the most recent empirical examples from panel data literature, data sets are also provided as well as the programs to implement the estimation and testing procedures described in the book. These programs will be made available via an accompanying website which will also contain solutions to end of chapter exercises that will appear in the book.

The text has been fully updated with new material on dynamic panel data models and recent results on non-linear panel models and in particular work on limited dependent variables panel data models.

Author Notes

Badi H. Baltagi is Professor of Econometrics at Texas A&M University, USA

Preface	p. xi
1 Introduction	p. 1
1.1 Panel Data: Some Examples	p. 1
1.2 Why Should We Use Panel Data? Their Benefits and Limitations	p. 6
Note	p. 11
2 The One-way Error Component Regression Model	p. 13
2.1 Introduction	p. 13
2.2 The Fixed Effects Model	p. 14
2.3 The Random Effects Model	p. 17
2.4 Maximum Likelihood Estimation	p. 22
2.5 Prediction	p. 23
2.6 Examples	p. 24
2.7 Selected Applications	p. 31
2.8 Computational Note	p. 31
Notes	p. 31
Problems	p. 32
3 The Two-way Error Component Regression Model	p. 35
3.1 Introduction	p. 35
3.2 The Fixed Effects Model	p. 35
3.3 The Random Effects Model	p. 37
3.4 Maximum Likelihood Estimation	p. 42
3.5 Prediction	p. 44
3.6 Examples	p. 45
3.7 Selected Applications	p. 48
Notes	p. 50
Problems	p. 50
4 Test of Hypotheses with Panel Data	p. 57
4.1 Tests for Poolability of the Data	p. 57
4.2 Tests for Individual and Time Effects	p. 63
4.3 Hausman's Specification Test	p. 72
4.4 Further Reading	p. 81
Notes	p. 82
Problems	p. 82
5 Heteroskedasticity and Serial Correlation in the Error Component Model	p. 87
5.1 Heteroskedasticity	p. 87
5.2 Serial Correlation	p. 92
Notes	p. 112
Problems	p. 113
6 Seemingly Unrelated Regressions with Error Components	p. 115
6.1 The One-way Model	p. 115
6.2 The Two-way Model	p. 116
6.3 Applications and Extensions	p. 117
Problems	p. 119
7 Simultaneous Equations with Error Components	p. 121
7.1 Single Equation Estimation	p. 121
7.2 Empirical Example: Crime in North Carolina	p. 124
7.3 System Estimation	p. 130
7.4 The Hausman and Taylor Estimator	p. 133
7.5 Empirical Example: Earnings Equation Using PSID Data	p. 136
7.6 Further Reading and Extensions	p. 140
Notes	p. 141
Problems	p. 142
8 Dynamic Panel Data Models	p. 147
8.1 Introduction	p. 147
8.2 The Arellano and Bond Estimator	p. 149
8.3 The Arellano and Bover Estimator	p. 155
8.4 The Ahn and Schmidt Moment Conditions	p. 158
8.5 The Blundell and Bond System GMM Estimator	p. 160
8.6 The Keane and Runkle Estimator	p. 162
8.7 Further Developments	p. 164
8.8 Empirical Examples	p. 170
8.9 Further Reading	p. 173
Notes	p. 178
Problems	p. 179
9 Unbalanced Panel Data Models	p. 181
9.1 Introduction	p. 181
9.2 The Unbalanced One-way Error Component Model	p. 181
9.3 Empirical Example: Hedonic Housing	p. 187
9.4 The Unbalanced Two-way Error Component Model	p. 191
9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data	p. 193
9.6 The Unbalanced Nested Error Component Model	p. 196
Notes	p. 200
Problems	p. 201
10 Special Topics	p. 205
10.1 Measurement Error and Panel Data	p. 205
10.2 Rotating Panels	p. 208
10.3 Pseudo-panels	p. 210
10.4 Alternative Methods of Pooling Time Series of Cross-Section Data	p. 214
10.5 Spatial Panels	p. 216
10.6 Short-run vs. Long-run Estimates in Pooled Models	p. 219
10.7 Heterogeneous Panels	p. 220
10.8 Count Panel Data	p. 226
Notes	p. 233
Problems	p. 233
11 Limited Dependent Variables and Panel Data	p. 237
11.1 Fixed and Random Logit and Probit Models	p. 237
11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data	p. 245
11.3 Dynamic Panel Data Limited Dependent Variable Models	p. 246
11.4 Selection Bias in Panel Data	p. 251
11.5 Censored and Truncated Panel Data Models	p. 256
11.6 Empirical Applications	p. 260
11.7 Empirical Example: Nurses Labor Supply	p. 262
11.8 Further Reading	p. 266
Notes	p. 268
Problems	p. 269
12 Nonstationary Panels	p. 273
12.1 Introduction	p. 273
12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence	p. 275
12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence	p. 284
12.4 Spurious Regression in Panel Data	p. 287
12.5 Panel Cointegration Tests	p. 292
12.6 Estimation and Inference in Panel Cointegration Models	p. 298
12.7 Empirical Example: Purchasing Power Parity	p. 301
12.8 Further Reading	p. 303
Notes	p. 308
Problems	p. 308
References	p. 311
Index	p. 337

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