Econometric Analysis of Panel Data / Edition 4

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Overview

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.  

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What People Are Saying


"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 it's 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.
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Product Details

  • ISBN-13: 9780470518861
  • Publisher: Wiley, John & Sons, Incorporated
  • Publication date: 6/30/2008
  • Edition description: New
  • Edition number: 4
  • Pages: 366
  • Sales rank: 1,495,832
  • Product dimensions: 6.50 (w) x 9.50 (h) x 0.80 (d)

Meet the Author

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

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 Fixed Effects Model 14

2.3 The Random Effects Model 17

2.4 Maximum Likelihood Estimation 22

2.5 Prediction 23

2.6 Examples 24

2.7 Selected Applications 31

2.8 Computational Note 31

Notes 31

Problems 32

3 The Two-way Error Component Regression Model 35

3.1 Introduction 35

3.2 The Fixed Effects Model 35

3.3 The Random Effects Model 37

3.4 Maximum Likelihood Estimation 42

3.5 Prediction 44

3.6 Examples 45

3.7 Selected Applications 48

Notes 50

Problems 50

4 Test of Hypotheses with Panel Data 57

4.1 Tests for Poolability of the Data 57

4.2 Tests for Individual and Time Effects 63

4.3 Hausman's Specification Test 72

4.4 Further Reading 81

Notes 82

Problems 82

5 Heteroskedasticity and Serial Correlation in the Error Component Model 87

5.1 Heteroskedasticity 87

5.2 Serial Correlation 92

Notes 112

Problems 113

6 Seemingly Unrelated Regressions with Error Components 115

6.1 The One-way Model 115

6.2 The Two-way Model 116

6.3 Applications and Extensions 117

Problems 119

7 Simultaneous Equations with Error Components 121

7.1 Single Equation Estimation 121

7.2 Empirical Example: Crime in North Carolina 124

7.3 System Estimation 130

7.4 The Hausman and Taylor Estimator 133

7.5 Empirical Example: Earnings Equation Using PSID Data 136

7.6 Further Reading and Extensions 140

Notes 141

Problems 142

8 Dynamic Panel Data Models 147

8.1 Introduction 147

8.2 The Arellano and Bond Estimator 149

8.3 The Arellano and Bover Estimator 155

8.4 The Ahn and Schmidt Moment Conditions 158

8.5 The Blundell and Bond System GMM Estimator 160

8.6 The Keane and Runkle Estimator 162

8.7 Further Developments 164

8.8 Empirical Examples 170

8.9 Further Reading 173

Notes 178

Problems 179

9 Unbalanced Panel Data Models 181

9.1 Introduction 181

9.2 The Unbalanced One-way Error Component Model 181

9.3 Empirical Example: Hedonic Housing 187

9.4 The Unbalanced Two-way Error Component Model 191

9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data 193

9.6 The Unbalanced Nested Error Component Model 196

Notes 200

Problems 201

10 Special Topics 205

10.1 Measurement Error and Panel Data 205

10.2 Rotating Panels 208

10.3 Pseudo-panels 210

10.4 Alternative Methods of Pooling Time Series of Cross-Section Data 214

10.5 Spatial Panels 216

10.6 Short-run vs. Long-run Estimates in Pooled Models 219

10.7 Heterogeneous Panels 220

10.8 Count Panel Data 226

Notes 233

Problems 233

11 Limited Dependent Variables and Panel Data 237

11.1 Fixed and Random Logit and Probit Models 237

11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data 245

11.3 Dynamic Panel Data Limited Dependent Variable Models 246

11.4 Selection Bias in Panel Data 251

11.5 Censored and Truncated Panel Data Models 256

11.6 Empirical Applications 260

11.7 Empirical Example: Nurses Labor Supply 262

11.8 Further Reading 266

Notes 268

Problems 269

12 Nonstationary Panels 273

12.1 Introduction 273

12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence 275

12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence 284

12.4 Spurious Regression in Panel Data 287

12.5 Panel Cointegration Tests 292

12.6 Estimation and Inference in Panel Cointegration Models 298

12.7 Empirical Example: Purchasing Power Parity 301

12.8 Further Reading 303

Notes 308

Problems 308

References 311

Index 337

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