Analysis of Panel Data / Edition 2by Cheng Hsiao
Pub. Date: 01/01/2004
Publisher: Cambridge University Press
Panel data models have become increasingly popular among applied researchers due to their heightened capacity for capturing the complexity of human behavior, as compared to cross-sectional or time series data models. This second edition represents a substantial revision of the highly successful first edition (1986). Recent advances in panel data research are… See more details below
Panel data models have become increasingly popular among applied researchers due to their heightened capacity for capturing the complexity of human behavior, as compared to cross-sectional or time series data models. This second edition represents a substantial revision of the highly successful first edition (1986). Recent advances in panel data research are presented in an accessible manner and are carefully integrated with the older material. The thorough discussion of theory and the judicious use of empirical examples make this book useful to graduate students and advanced researchers in economics, business, sociology and political science.
Table of Contents
Part I. Introduction: 1. Advantages of panel data; 2. Issues involved in utilizing panel data; 3. Outline of the monograph; Part II. Analysis of Covariance: 4. Introduction; 5. Analysis of covariance; 6. An example; Part III. Simple Regression with Variable Intercepts: 7. Introduction; 8. Fixed-effects models: least-squares dummy-variable approach; 9. Random-effects models: estimation of variance-components models; 10. Fixed effects or random effects; 11. Tests for misspecification; 12. Models with specific variables and both individual- and time-specific effects; 13. Heteroscedasticity; 14. Models with serially correlated errors; 15. Models with arbitrary error structure - Chamberlain - approach; Part IV. Dynamic Models with Variable Intercepts: 16. Introduction; 17. The covariance estimator; 18. Random-effects models; 19. An example - demand for natural gas; 20. Fixed effects models; 21. Estimation of dynamic models with arbitrary correlations in the residuals; 22. Fixed effects vector autoregressive models; Part V. Simultaneous-Equations Models: 23. Introduction; 24. Joint generalized-least squares estimation technique; 25. Estimation of structural equations; 26. Triangular system; Part VI. Variable-Coefficient Models: 27. Introduction; 28. Coefficients that vary over cross-sectional units; 29. Coefficients that vary over time and cross-sectional units; 30. Coefficients that evolve over time; 31. Coefficients that are functions of other exogenous variables; 32. A mixed fixed and random coefficients model; 33. Dynamic random coefficients models; 34. An example - liquidity constraints and firm investment expenditure; Part VII. Discrete Data: 35. Introduction; 36. Some discrete-response models; 37. The parametric approach to static models with heterogeneity; 38. The semiparametric approach to static models; 39. Dynamic models; Part VIII. Truncated and Censored Data: 40. Introduction; 41. Nonrandomly missing data; 42. Tobit models with random individual effects; 43. Fixed effects estimator; 44. An example: housing expenditure; 45. Dynamic Tobit models; Part IX. Incomplete Panel Data: 46. Estimating distributed lags in short panels; 47. Rotating or randomly missing data; 48. Pseudo panels (or repeated cross-sectional data); 49. Pooling of a single cross-section and a single time series; Part X. Miscellaneous Topics: 50. Simulation methods; 51. Panels with large N and T; 52. Unit root tests; 53. Data with multi-level structures; 54. Errors of measurement; 55. Modeling cross-sectional dependence; Part XI. A Summary View: 56. Introduction; 57. Benefits and limitations of panel data; 58. Efficiency of the estimates.
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