Revision of established textbook by one of the leadinginternational researchers and writers on panel data.
Comments on the current fourth edition from leadingeconometricians.

Includes new chapter entitled Spatial Panel Data
New empirical applications have replaced some of the olderones.

Panel data econometrics has evolved rapidly over the last decade. Micro and Macro panels are increasing in numbers and availability and methods to deal with these data are in high demand from practitioners. 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 has been fully revised and updated and includes: * A new chapter entitled Spatial Panel Data * New empirical applications * New material on non-stationary panels. * New empirical applications using Stata and EViews. * Thoroughly updated References. * Additional exercises in each chapter This is a definitive book written by one of the architects of modern, panel data econometrics. It provides both a practical introduction to the subject matter, as well as a thorough discussion of the underlying statistical principles without taxing the reader too greatly. Since its first publication in 1995, it has quickly become a standard accompanying text in advanced econometrics courses around the world, and a major reference for researchers doing empirical work with longitudinal data." Professor Kajal Lahiri, State University of New York, Albany, USA. "Econometric Analysis of Panel Data is a classic in its field, used by researchers and graduate students throughout the world. In this new edition, Professor Baltagi has incorporated extensive new material, reflecting recent advances in the panel data literature in areas such as dynamic (including non-stationary) and limited dependent variable panel data models. It is an invaluable read for anyone interested in panel data." Professor Gary Koop, University of Strathclyde, UK. "This book is the most comprehensive work available on panel data. It is written by one of the leading contributors to the field, and is notable for its encyclopaedic coverage and its clarity of exposition. It is useful to theorists and to people doing applied work using panel data. It is valuable as a text for a course in panel data, as a supplementary text for more general courses in econometrics, and as a reference." Professor Peter Schmidt, Michigan State University, USA.

Preface xi

1 Introduction 1

1.1 Panel Data: Some Examples 1

1.2 Why Should We Use Panel Data? Their Benefits and Limitations 6

Note 11

2 The One-way Error Component Regression Model 13

2.1 Introduction 13

2.2 The One-way Fixed Effects Model 14

2.3 The One-way Random Effects Model 20

2.4 Maximum Likelihood Estimation 25

2.5 Prediction 26

2.6 Examples 27

2.7 Selected Applications 34

2.8 Computational Note 34

Notes 34

Problems 35

3 The Two-way Error Component Regression Model 39

3.1 Introduction 39

3.2 The Two-way Fixed Effects Model 39

3.3 The Two-way Random Effects Model 42

3.4 Maximum Likelihood Estimation 47

3.5 Prediction 49

3.6 Examples 50

3.7 Computational Note 53

Notes 55

Problems 55

4 Test of Hypotheses with Panel Data 63

4.1 Tests for Poolability 63

4.2 Tests for Individual and Time Effects 68

4.3 Hausman’s Specification Test 76

4.4 Further Reading 86

Notes 88

Problems 88

5 Heteroskedasticity and Serial Correlation in the Error Component Model 91

5.1 Heteroskedasticity 91

5.2 Serial Correlation 96

5.3 Time-wise Autocorrelated and Cross-sectionally Heteroskedastic

Panel Regression 115

5.4 Further Reading 119

Notes 119

Problems 120

6 Seemingly Unrelated Regressions with Error Components 123

6.1 The One-way Model 123

6.2 The Two-way Model 124

6.3 Applications and Extensions 125

Problems 127

7 Simultaneous Equations with Error Components 129

7.1 Single Equation Estimation 129

7.2 Empirical Example: Crime in North Carolina 132

7.3 System Estimation 138

7.4 The Hausman and Taylor Estimator 141

7.5 Empirical Example: Earnings Equation Using PSID Data 144

7.6 Further Reading 147

Notes 150

Problems 150

8 Dynamic Panel Data Models 155

8.1 Introduction 155

8.2 The Arellano and Bond Estimator 157

8.3 The Arellano and Bover Estimator 161

8.4 The Ahn and Schmidt Moment Conditions 164

8.5 The Blundell and Bond System GMM Estimator 167

8.6 The Keane and Runkle Estimator 168

8.7 Limited Information Maximum Likelihood 171

8.8 Further Developments 172

8.9 Empirical Examples 175

8.10 Selected Applications 179

8.11 Further Reading 182

Notes 183

Problems 183

9 Unbalanced Panel Data Models 187

9.1 Introduction 187

9.2 The Unbalanced One-way Error Component Model 187

9.3 Empirical Example: Hedonic Housing 194

9.4 The Unbalanced Two-way Error Component Model 197

9.5 Testing for Individual and Time Effects Using

Unbalanced Panel Data 200

9.6 The Unbalanced Nested Error Component Model 203

Notes 208

Problems 209

10 Special Topics 213

10.1 Measurement Error and Panel Data 213

10.2 Rotating Panels 216

10.3 Pseudo Panels 218

10.4 Short-run versus Long-run Estimates in Pooled Models 221

10.5 Heterogeneous Panels 222

10.6 Count Panel Data 228

Notes 235

Problems 235

11 Limited Dependent Variables and Panel Data 239

11.1 Fixed and Random Logit and Probit Models 239

11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data 247

11.3 Dynamic Panel Data Limited Dependent Variable Models 248

11.4 Selection Bias in Panel Data 254

11.5 Censored and Truncated Panel Data Models 258

11.6 Applications 263

11.7 Empirical Example: Nurses’ Labour Supply 265

11.8 Further Reading 268

Notes 270

Problems 271

12 Nonstationary Panels 275

12.1 Introduction 275

12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence 277

12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence 287

12.4 Spurious Regression in Panel Data 291

12.5 Panel Cointegration Tests 293

12.6 Estimation and Inference in Panel Cointegration Models 299

12.7 Empirical Examples 303

12.8 Further Reading 309

Notes 315

Problems 315

13 Spatial Panel Data Models 319

13.1 Introduction 319

13.2 Spatial Error Component Regression Model 319

13.3 Spatial Lag Panel Data Regression Model 325

13.4 Forecasts using Panel Data with Spatial Error Correlation 329

13.5 Panel Unit Root Tests and Spatial Dependence 330

13.6 Panel Data Tests for Cross-sectional Dependence 332

13.7 Further Reading 336

Problems 337

References 339

Appendix 361

Index 000