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Handbook of econometrics : volume 5 / edited by James J. Heckman and Edward Leamer
Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Handbooks in economics ; 2Publisher: Amsterdam ; London : North Holland, 2001Description: Online RessourceISBN:- 9780444823403
- 0444823409
- 330/.01/51195
- 330.015195
- HB139 .H36 2001
- HB139
Contents:
Summary: The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics coursesPPN: PPN: 825903521Package identifier: Produktsigel: ZDB-1-HBE-ebook | ZDB-1-HBE | ZDB-33-EBS | ZDB-33-ESD | BSZ-33-EBS-HSAA
Part 11: New Developments in Theoretical Econometrics. 52. The bootstrap (J. Horowitz). 53. Panel data models: some recent developments (M. Arellano, B. Honor & eacute;). 54. Interactions-based models (W.A. Brock, S.N. Durlauf). 55. Duration models: specification, identification, and multiple durations (G.J. van den Berg). Part 12: Computational Methods in Econometrics. 56. Computational intensive methods for integration in econometrics (J. Geweke, M. Keane). 57. Markov chain Monte Carlo methods: computation and inference (S. Chib). Part 13: Applied Econometrics. 58. Calibration (C. Dawkins, T.N. Srinivasan and J. Whalley). 59. Measurement error in survey data (J. Bound, C. Brown and N. Mathiowetz).
Cover; Copyright Page; CONTENTS; Introduction to the Series; Contents of the Handbook; Preface to the Handbook; References; Part 11: NEW DEVELOPMENTS IN THEORETICAL ECONOMETRICS; Chapter 52. The Bootstrap; Abstract; Keywords; 1. Introduction; 2. The bootstrap sampling procedure and its consistency; 3. Asymptotic refinements; 4. Extensions; 5. Monte Carlo experiments; 6. Conclusions; Acknowledgements; Appendix A. Informal derivation of Equation (3.27); References; Chapter 53. Panel Data Models: Some Recent Developments; Abstract; Keywords; 1. Introduction
2. Linear models with predetermined variables: identification3. Linear models with predetermined variables: estimation; 4. Nonlinear panel data models; 5. Conditional maximum likelihood estimation; 6. Discrete choice models with ?fixedŽ effects; 7. Tobit-type models with ?fixedŽ effects; 8. Models with lagged dependent variables; 9. ?RandomŽ effects models; 10. Concluding remarks; References; Chapter 54. Interactions-Based Models; Abstract; Keywords; 1. Introduction; 2. Binary choice with social interactions; 3. Identification: basic issues; 4. Further topics in identification
5. Sampling properties6. Statistical analysis with grouped data; 7. Evidence; 8. Summary and conclusions; Appendix A; References; Chapter 55. Duration Models: Specification, Identification and Multiple Durations; Abstract; Keywords; 1. Introduction; 2. Basic concepts and notation; 3. Some structural models of durations; 4. The Mixed Proportional Hazard model; 5. Identification of the MPH model with single-spell data; 6. The MPH model with multi-spell data; 7. An informal classification of reduced-form multiple-duration models; 8. The Multivariate Mixed Proportional Hazard model
9. Causal duration effects and selectivity10. Conclusions and recommendations; References; Part 12: COMPUTATIONAL METHODS IN ECONOMETRICS; Chapter 56. Computationally Intensive Methods for Integration in Econometrics; Abstract; Keywords; 1. Introduction; 2. Monte Carlo methods of integral approximation; 3. Approximate solution of discrete dynamic optimization problems; 4. Classical simulation estimation of the multinomial probit model; 5. Univariate latent linear models; 6. Multivariate latent linear models; 7. Bayesian inference for a dynamic discrete choice model
Appendix A. The full univariate latent linear modelAppendix B. The full multivariate latent linear model; References; Chapter 57. Markov Chain Monte Carlo Methods: Computation and Inference; Abstract; Keywords; 1. Introduction; 2. Classical sampling methods; 3. Markov chains; 4. Metropolis-Hastings algorithm; 5. The Gibbs sampling algorithm; 6. Sampler performance and diagnostics; 7. Strategies for improving mixing; 8. MCMC algorithms in Bayesian estimation; 9. Sampling the predictive density; 10. MCMC methods in model choice problems; 11. MCMC methods in optimization problems
12. Concluding remarks
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