Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made.
1. General Introduction - p. xix 2. Chapter 1: A Synopsis of Econometrics - p. xxvii 3. Chapter 2: Testing and Correcting for Endogeneity in Nonlinear Unobserved Effects Models - p. 20 4. Chapter 3: Nonlinear and Related Panel Data Models - p. 43 5. Abstract - p. 45 6. 1 Introduction - p. 45 7. 2 Nonlinear Models - p. 48 8. 3 Panel Data Models - p. 53 9. 4 Nonlinear Panel Data Modeling - p. 61 10. 5 Panel Data - p. 76 11. 6 Modeling Frameworks and Applications - p. 78 12. Chapter 4: Nonparametric Estimation and Inference for Panel Data Models - p. 96 13. Abstract - p. 97 14. Acknowledgments - p. 97 15. 1 Introduction - p. 97 16. 2 How Unobserved Heterogeneity Complicates Estimation - p. 98 17. 3 Estimation in the Random Effects Framework - p. 99 18. 4 Estimation in the Fixed Effects Framework - p. 108 19. 5 Dynamic Panel Estimation - p. 116 20. 6 Inference - p. 118 21. 7 Conclusions - p. 127 22. Chapter 5: Heterogeneity and Endogeneity in Panel Stochastic Frontier Models - p. 129 23. Chapter 6: Bayesian Estimation of Panel Count Data Models: Dynamics, Latent Heterogeneity, Serial Error Correlation, and Nonparametric Structures - p. 146 24. Abstract - p. 147 25. 1 Introduction - p. 147 26. 2 Bayesian Preliminaries - p. 150 27. 3 Parametric Panel Count Data Regression Models - p. 157 28. 4 Semiparametric Panel Count Data Regression Models - p. 160 29. 5 Prior-Posterior Analysis - p. 161 30. 6 Conclusions - p. 171 31. Chapter 7: Fixed Effects Likelihood Approach for Large Panels - p. 173 32. Abstract - p. 175 33. Acknowledgment - p. 175 34. 1 Introduction - p. 175 35. 2 Notations and Preliminaries - p. 177 36. 3 Fixed Dimensional Case - p. 179 37. 4 Panel With Individual Effects - p. 180 38. 5 Panel With Individual Effects and Time Effects - p. 188 39. 6 Conclusions - p. 194 40. Chapter 8: Panel Vector Autoregressions With Binary Data - p. 196 41. Abstract - p. 197 42. Acknowledgments - p. 197 43. 1 Introduction - p. 197 44. 2 The Univariate Logit Model - p. 199 45. 3 The Bivariate Pure VAR(1) Logit Case - p. 204 46. 4 The Bivariate Logit Model With Exogenous Covariates - p. 209 47. 5 The General M-Variate, General T VAR(1) Case - p. 212 48. 6 Contemporaneous Cross-Equation Dependence - p. 213 49. 7 Monte Carlo Experiments - p. 218 50. 8 Conclusions - p. 220 51. Chapter 9: Implementing Generalized Panel Data Stochastic Frontier Estimators - p. 223 52. Chapter 10: Panel Cointegration Techniques and Open Challenges - p. 249 53. Chapter 11: Alternative Approaches to the Econometrics of Panel Data - p. 287 54. Abstract - p. 289 55. 1 Introduction - p. 290 56. 2 Models With Nonunique Coefficients and Error Terms for Panel Data - p. 292 57. 3 Models With Unique Coefficients and Error Terms for Panel Data - p. 296 58. 4 Bayesian Analysis of Panel Data - p. 323 59. 5 Empirical Evidence of the Causal Effects of Wives’ Education on Their Earnings - p. 330 60. 6 Conclusions - p. 339 61. Appendix A Proof of the Uniqueness of the Coefficients and Error term of a Stochastic Law - p. 340 62. Appendix B Conditions for the Consistency of Certain Estimators of the Coefficients of a Stochastic Law - p. 341 63. Chapter 12: Analysis of Panel Data Using R - p. 317 64. Index - p. 397
Panel Data Econometrics: Theory introduces econometric modelling. Written by experts from diverse disciplines, the volume uses longitudinal datasets to illuminate applications for a variety of fields, such as banking, financial markets, tourism and transportation, auctions, and experimental economics. Contributors emphasize techniques and applications, and they accompany their explanations with case studies, empirical exercises and supplementary code in R. They also address panel data analysis in the context of productivity and efficiency analysis, where some of the most interesting applications and advancements have recently been made.
1. General Introduction - p. xix 2. Chapter 1: A Synopsis of Econometrics - p. xxvii 3. Chapter 2: Testing and Correcting for Endogeneity in Nonlinear Unobserved Effects Models - p. 20 4. Chapter 3: Nonlinear and Related Panel Data Models - p. 43 5. Abstract - p. 45 6. 1 Introduction - p. 45 7. 2 Nonlinear Models - p. 48 8. 3 Panel Data Models - p. 53 9. 4 Nonlinear Panel Data Modeling - p. 61 10. 5 Panel Data - p. 76 11. 6 Modeling Frameworks and Applications - p. 78 12. Chapter 4: Nonparametric Estimation and Inference for Panel Data Models - p. 96 13. Abstract - p. 97 14. Acknowledgments - p. 97 15. 1 Introduction - p. 97 16. 2 How Unobserved Heterogeneity Complicates Estimation - p. 98 17. 3 Estimation in the Random Effects Framework - p. 99 18. 4 Estimation in the Fixed Effects Framework - p. 108 19. 5 Dynamic Panel Estimation - p. 116 20. 6 Inference - p. 118 21. 7 Conclusions - p. 127 22. Chapter 5: Heterogeneity and Endogeneity in Panel Stochastic Frontier Models - p. 129 23. Chapter 6: Bayesian Estimation of Panel Count Data Models: Dynamics, Latent Heterogeneity, Serial Error Correlation, and Nonparametric Structures - p. 146 24. Abstract - p. 147 25. 1 Introduction - p. 147 26. 2 Bayesian Preliminaries - p. 150 27. 3 Parametric Panel Count Data Regression Models - p. 157 28. 4 Semiparametric Panel Count Data Regression Models - p. 160 29. 5 Prior-Posterior Analysis - p. 161 30. 6 Conclusions - p. 171 31. Chapter 7: Fixed Effects Likelihood Approach for Large Panels - p. 173 32. Abstract - p. 175 33. Acknowledgment - p. 175 34. 1 Introduction - p. 175 35. 2 Notations and Preliminaries - p. 177 36. 3 Fixed Dimensional Case - p. 179 37. 4 Panel With Individual Effects - p. 180 38. 5 Panel With Individual Effects and Time Effects - p. 188 39. 6 Conclusions - p. 194 40. Chapter 8: Panel Vector Autoregressions With Binary Data - p. 196 41. Abstract - p. 197 42. Acknowledgments - p. 197 43. 1 Introduction - p. 197 44. 2 The Univariate Logit Model - p. 199 45. 3 The Bivariate Pure VAR(1) Logit Case - p. 204 46. 4 The Bivariate Logit Model With Exogenous Covariates - p. 209 47. 5 The General M-Variate, General T VAR(1) Case - p. 212 48. 6 Contemporaneous Cross-Equation Dependence - p. 213 49. 7 Monte Carlo Experiments - p. 218 50. 8 Conclusions - p. 220 51. Chapter 9: Implementing Generalized Panel Data Stochastic Frontier Estimators - p. 223 52. Chapter 10: Panel Cointegration Techniques and Open Challenges - p. 249 53. Chapter 11: Alternative Approaches to the Econometrics of Panel Data - p. 287 54. Abstract - p. 289 55. 1 Introduction - p. 290 56. 2 Models With Nonunique Coefficients and Error Terms for Panel Data - p. 292 57. 3 Models With Unique Coefficients and Error Terms for Panel Data - p. 296 58. 4 Bayesian Analysis of Panel Data - p. 323 59. 5 Empirical Evidence of the Causal Effects of Wives’ Education on Their Earnings - p. 330 60. 6 Conclusions - p. 339 61. Appendix A Proof of the Uniqueness of the Coefficients and Error term of a Stochastic Law - p. 340 62. Appendix B Conditions for the Consistency of Certain Estimators of the Coefficients of a Stochastic Law - p. 341 63. Chapter 12: Analysis of Panel Data Using R - p. 317 64. Index - p. 397
