Beschreibung
The aim of this volume is to provide a general overview of the econometrics of panel data, both from a theoretical and from an applied viewpoint. Since the pioneering papers by Edwin Kuh (1959), Yair Mundlak (1961), Irving Hoch (1962), and Pietro Balestra and Marc Nerlove (1966), the pooling of cross sections and time series data has become an increasingly popular way of quantifying economic relationships. Each series provides information lacking in the other, so a combination of both leads to more accurate and reliable results than would be achievable by one type of series alone. Over the last 30 years much work has been done: investigation of the properties of the applied estimators and test statistics, analysis of dynamic models and the effects of eventual measurement errors, etc. These are just some of the problems addressed by this work. In addition, some specific diffi culties associated with the use of panel data, such as attrition, heterogeneity, selectivity bias, pseudo panels etc., have also been explored. The first objective of this book, which takes up Parts I and II, is to give as complete and up-to-date a presentation of these theoretical developments as possible. Part I is concerned with classical linear models and their extensions; Part II deals with nonlinear models and related issues: logit and pro bit models, latent variable models, duration and count data models, incomplete panels and selectivity bias, point processes, and simulation techniques.
Inhaltsverzeichnis
InhaltsangabePreface. 1. Formulation and estimation of econometric models for panel data; M. Nerlove, P. Balestra. Part I: Linear models. 2. Introduction to linear models for panel data; P. Balestra. 3. Fixed effect models and fixed coefficient models; P. Balestra. 4. Error components models; L. Mátyás. 5. Random coefficients models; C. Hsiao. 6. Linear models with random regressors; P. Sevestre, A. Trognon. 7. Dynamic linear models; P. Sevestre, A. Trognon. 8. Dynamic linear models for heterogenous panels; H. Pesaran, et al. 9. Simultaneous equations; J. Krishnakumar. 10. Panel data with measurement errors; E. Biørn. 11. Pseudo panel data; M. Verbeek. 12. Specification issues; B.H. Baltagi. 13. The pooling problem; G.S. Maddala, Wanhong Hu. 14. The Chamberlain approach; B. Crépon, J. Mairesse. Appendix: Matrix algebra for linear models. Part II: Nonlinear models. 15. Introduction to nonlinear models; C. Gourieroux. 16. Logit and probit models; Cheng Hsiao. 17. Nonlinear latent variable models; Cheng Hsiao. 18. Incomplete panels and selection bias; M. Verbeek, T. Nijman. 19. Duration Models; J.- P. Florens, et al. 20. Point processes; J.-P. Florens, D. Fougère. 21. Improved estimation procedures; O. Lieberman, L. Mátyás. 22. Some GMM Estimation methods and specification tests for nonlinear models; M. Lechner, J.Breitung. 23. Simulation techniques; J.-F. Richard. 24. Inference in panel data models via Gibbs sampling; S. Chib. Part III: Selected applications. Introduction to the applications; Z. Griliches. 25. Dynamic labour demand models; G. Bresson, et al. 26. Econometric models of company investment; R. Blundell, et al. 27. Consumption dynamics and panel data: a survey; J.-M. Robin. 28. Estimation of labour supply functions using panel data: a survey; F. Laisney, et al. 29. Individual labour market transitions; D. Fougère, T. Kamionka. 30. Modelling companies' dividend policy using account panel data; J.-F. Malécot. 31. Panel data, multinational enterprises and direct investment; C. Mathieu. 32. Production frontiers and efficiency measurement; C. Cornwell, P. Schmidt. 33. Software review; P. Blanchard. Index.
