Recent Developments in the Econometrics of Panel Data

Edited by Badi H. Baltagi, Distinguished Professor of Economics and CPR Senior Research Associate, Center for Policy Research, Syracuse University, US

• Contents:Volume IAcknowledgementsIntroduction Badi H. Baltagi PART IDYNAMIC PANELS AND GMM 1. Seung C. Ahn and Peter Schmidt (1995), ‘Efficient Estimation of Models for Dynamic Panel Data’2. Seung C. Ahn and Peter Schmidt (1997), ‘Efficient Estimation of Dynamic Panel Data Models: Alternative Assumptions and Simplified Estimation’3. César Alonso-Borrego and Manuel Arellano (1999), ‘Symmetrically Normalized Instrumental-Variable Estimation Using Panel Data’4. Manuel Arellano and Olympia Bover (1995), ‘Another Look at the Instrumental Variable Estimation of Error-Components Models’5. Richard Blundell and Stephen Bond (1998), ‘Initial Conditions and Moment Restrictions in Dynamic Panel Data Models’6. Bruno Crepon, Francis Kramarz and Alain Trognon (1997), ‘Parameters of Interest, Nuisance Parameters and Orthogonality Conditions: An Application to Autoregressive Error Component Models’7. Jinyong Hahn (1999), ‘How Informative is the Initial Condition in the Dynamic Panel Model with Fixed Effects?’8. Jan F. Kiviet (1995), ‘On Bias, Inconsistency, and Efficiency of Various Estimators in Dynamic Panel Data Models’9. Tom Wansbeek (2001), ‘GMM Estimation in Panel Data Models with Measurement Error’10. James P. Ziliak (1997), ‘Efficient Estimation with Panel Data when Instruments are Predetermined: An Empirical Comparison of Moment-Condition Estimators’PART IIHETEROGENEOUS PANELS 11. Badi H. Baltagi and James M. Griffin (1997), ‘Pooled Estimators vs. their Heterogeneous Counterparts in the Context of Dynamic Demand for Gasoline’12. Cheng Hsiao and A. Kamil Tahmiscioglu (1997), ‘A Panel Analysis of Liquidity Constraints and Firm Investment’13. G.S. Maddala, Robert P. Trost, Hongyi Li and Frederick Joutz (1997), ‘Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators’14. M. Hashem Pesaran and Ron Smith (1995), ‘Estimating Long-Run Relationships from Dynamic Heterogeneous Panels’PART IIINON-STATIONARY PANELS 15. Kaddour Hadri (2000), ‘Testing for Stationarity in Heterogeneous Panel Data’16. Richard D.F. Harris and Elias Tzavalis (1999), ‘Inference for Unit Roots in Dynamic Panels Where the Time Dimension is Fixed’17. Chihwa Kao (1999), ‘Spurious Regression and Residual-Based Tests for Cointegration in Panel Data’18. G.S. Maddala and Shaowen Wu (1999), ‘A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test’19. Hyungsik R. Moon and Peter C.B. Phillips (1999), ‘Maximum Likelihood Estimation in Panels with Incidental Trends’20. Peter Pedroni (2000), ‘Fully Modified OLS for Heterogeneous Cointegrated Panels’21. M. Hashem Pesaran, Yongcheol Shin, and Ron P. Smith (1999), ‘Pooled Mean Group Estimation of Dynamic Heterogeneous Panels’22. Peter C.B. Phillips and Hyungsik R. Moon (1999), ‘Linear Regression Limit Theory for Nonstationary Panel Data’Name IndexVolume II AcknowledgementsAn introduction by the editor to both volumes appears in Volume IPART ILIMITED DEPENDENT VARIABLES 1. Bo E. Honoré (1992), ‘Trimmed LAD and Least Squares Estimation of Truncated and Censored Regression Models with Fixed Effects’2. Bo E. Honoré and Ekaterini Kyriazidou (2000), ‘Panel Data Discrete Choice Models with Lagged Dependent Variables’3. Michael P. Keane (1994), ‘A Computationally Practical Simulation Estimator for Panel Data’4. Ekaterini Kyriazidou (1997), ‘Estimation of a Panel Data Sample Selection Model’5. Michael Lechner (1995), ‘Some Specification Tests for Probit Models Estimated on Panel Data’6. Myoung-jae Lee (1999), ‘A Root-N Consistent Semiparametric Estimator for Related-Effect Binary Response Panel Data’7. Francis Vella and Marno Verbeek (1999), ‘Two-step Estimation of Panel Data Models with Censored Endogenous Variables and Selection Bias’8. Jeffrey M. Wooldridge (1995), ‘Selection Corrections for Panel Data Models under Conditional Mean Independence Assumptions’PART IINON-LINEAR PANEL MODELS 9. Jason Abrevaya (1999), ‘Leapfrog Estimation of a Fixed-Effects Model with Unknown Transformation of the Dependent Variable’10. Jeffrey M. Wooldridge (1999), ‘Distribution-free Estimation of Some Nonlinear Panel Data Models’PART IIIUNBALANCED PANELS 11. Werner Antweiler (2001), ‘Nested Random Effects Estimation in Unbalanced Panel Data’12. Badi H. Baltagi and Ping X. Wu (1999), ‘Unequally Spaced Panel Data Regressions with AR (1) Disturbances’13. Badi H. Baltagi, Seuck Heun Song and Byoung Cheol Jung (2001), ‘The Unbalanced Nested Error Component Regression Model’14. Peter Davis (2002), ‘Estimating Multi-way Error Components Models with Unbalanced Data Structures’PART IVPSEUDO-PANELS 15. M. Dolores Collado (1997), ‘Estimating Dynamic Models from Time Series of Independent Cross-Sections’16. Sourafel Girma (2000), ‘A Quasi-Differencing Approach to Dynamic Modelling from a Time Series of Independent Cross-Sections’17. David J. McKenzie (2001), ‘Estimation of AR(1) Models with Unequally Spaced Pseudo-Panels’18. Robert Moffitt (1993), ‘Identification and Estimation of Dynamic Models with a Time Series of Repeated Cross-Sections’PART VSPECIFICATION TESTS IN PANELS 19. Badi H. Baltagi, Javier Hidalgo and Qi Li (1996), ‘A Nonparametric Test for Poolability Using Panel Data’20. Badi H. Baltagi and Qi Li (1995), ‘Testing AR(1) against MA(1) Disturbances in an Error Component Model’21. Anil K. Bera, Walter Sosa-Escudero and Mann Yoon (2001), ‘Tests for the Error Component Model in the Presence of Local Misspecification’22. Q. Li and C. Hsiao (1998), ‘Testing Serial Correlation in Semiparametric Panel Data Models’23. Gilbert E. Metcalf (1996), ‘Specification Testing in Panel Data with Instrumental Variables’Name Index

'The editor of volumes like the present one has the chance to direct the reader's attention to nearly unknown but relevant papers. Baltagi has done this job very well. . . Summing up, the two volumes on recent developments of panel data belong in every library. The collection helps the newcomer to better understand this topic and it is a useful reference for the expert.'