Panel Data Modeling and Inference: A Bayesian Primer

Advanced Studies in Theoretical and Applied Econometrics

Volumn 46, Issue , 2008, Pages 479-515

  Chib, Siddhartha ^a  

^a WASHINGTON UNIVERSITY IN ST LOUIS   (United States)

Author keywords

American Statistical Association; Marginal Likelihood; Markov Chain Monte Carlo; Posterior Density; Posterior Distribution

Indexed keywords

EID: 85064939602     PISSN: 15705811     EISSN: 22147977     Source Type: Book Series    
DOI: 10.1007/978-3-540-75892-1_15     Document Type: Chapter

References (38)

1. ALBERT, J. and S. CHIB (1993), Bayesian analysis of binary and polychotomous response data, Journal of the American Statistical Association, 88, 669–679.
2. ALBERT, J. and S. CHIB (1995), Bayesian residual analysis for binary response models, Biometrika, 82, 747–759.
3. BASU, S. and S. CHIB (2003), Marginal likelihood and Bayes factors for Dirichlet process mixture models, Journal of the American Statistical Association, 98, 224–235.
4. BESAG, J. (1974), Spatial interaction and the statistical analysis of lattice systems (with discus-sion), Journal of the Royal Statistical Society, B, 36, 192–236.
5. BUTLER, J. S. and R. MOFFITT (1982), A computationally efficient quadrature procedure for the one factor multinomial probit model, Econometrica, 50, 761–764.
6. CARLIN, B. P. and T. LOUIS (2000), Bayes and Empirical Bayes Methods for Data Analysis, 2nd ed., Chapman and Hall, New York.
7. CHALONER, K. and R. BRANT (1988), A Bayesian approach to outlier detection and residual analysis, Biometrika, 75, 651–659.
8. CHAMBERLAIN, G. (1980), Analysis of covariance with qualitative data, Review of Economic Studies, 47, 225–238.
9. CHEN, Z. and D. B. DUNSON (2003), Random effects selection in linear mixed models, Biometrics, 59, 762–769.
10. CHEN, M-H and Q-M. SHAO (1998), On Monte Carlo methods for estimating ratios of normalizing constants, Annals of Statistics, 25, 1563–1594.
11. CHIANG, J., S. CHIB and C. NARASIMHAN (1999), Markov Chain Monte Carlo and models of consideration set and parameter heterogeneity, Journal of Econometrics, 89, 223–248.
12. CHIB, S. (1992), Bayes regression for the Tobit censored regression model, Journal of Econometrics, 51, 79–99.
13. CHIB, S. (1995), Marginal likelihood from the Gibbs output, Journal of the American Statistical Association, 90, 1313–1321.
14. CHIB, S. (2001), Markov Chain Monte Carlo methods: Computation and inference, in Handbook of Econometrics volume 5 (eds., J. J. Heckman and E. Leamer), Amsterdam: North Holland 3569-3649.
15. CHIB, S. and E. GREENBERG (1994), Bayes inference for regression models with ARMA(p, q) errors, Journal of Econometrics, 64, 183–206.
16. CHIB, S. and E. GREENBERG (1995), Understanding the Metropolis–Hastings algorithm, American Statistician, 49, 327–335.
17. CHIB, S. and E. GREENBERG (1996), Markov chain Monte Carlo simulation methods in econometrics, Econometric Theory, 12, 409–431.
18. CHIB, S. E. GREENBERG, and R. WINKLEMNANN (1998), Posterior simulation and Bayes factors in panel count data models, Journal of Econometrics, 86, 33–54.
19. CHIB, S. and B. P. CARLIN (1999), On MCMC sampling in hierarchical longitudinal models, Statistics and Computing, 9, 17–26.
20. CHIB, S. and I. JELIAZKOV (2001), Marginal likelihood from the Metropolis–Hastings output, Journal of the American Statistical Association, 96, 270–281.
21. CHIB, S., P. B. SEETHARAMAN, and A. STRIJNEV (2004) Model of brand choice with a no-purchase option calibrated to scanner panel data, Journal of Marketing Research, 184–196.
22. CONGDON, P. (2001) Bayesian Statistical Modelling, John Wiley & Sons, Chichester.
23. DICICCIO, T. J., R. E., KASS, A. E., RAFTERY and L. WASSERMAN (1997), Computing Bayes factors by combining simulation and asymptotic approximations, Journal of the American Statistical Association, 92, 903–915.
24. GELFAND, A. E. and A. F. M. SMITH (1990), Sampling-based approaches to calculating marginal densities, Journal of the American Statistical Association, 85, 398–409.
25. GELFAND, A. E., S. K. SAHU, and B. P. CARLIN (1995), Efficient parameterizations for normal linear mixed models, Biometrika, 82, 479–488.
26. GEMAN, S. and D. GEMAN (1984), Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 609–628.
27. HASTINGS, W. K. (1970), Monte Carlo sampling methods using Markov chains and their applications, Biometrika, 57, 97–109.
28. HECKMAN, J.J. (1981), Statistical models for discrete panel data, in Structural Analysis of Discrete Data with Econometric Applications, (eds., C. F. Manski and D. McFadden), Cambridge: MIT Press, 114–178.
29. LINDLEY, D.V. and A.F.M. SMITH (1972), Bayes estimates for the linear model, Journal of the Royal Statistical Society B, 34, 1–41.
30. METROPOLIS, N., A. W. ROSENBLUTH, M. N. ROSENBLUTH, A. H. TELLER, and E. TELLER (1953), Equations of state calculations by fast computing machines, Journal of Chemical Physics, 21, 1087–1092.
31. PAGE, E (1977), Approximations to the cumulative normal function and its inverse for use on a pocket calculator, Applied Statistics, 26, 75–76.
32. ROBERTS, C.P. (2001), The Bayesian Choice, New York: Springer Verlag.
33. RIPLEY, B. (1987), Stochastic Simulation, New York: John Wiley & Sons.
34. STOUT, W. F. (1974), Almost Sure Convergence, New York: Academic Press.
35. TANNER, M. A. and W. H. WONG (1987), The calculation of posterior distributions by data augmentation, Journal of the American Statistical Association, 82, 528–549.
36. TIERNEY, L. (1994), Markov chains for exploring posterior distributions (with discussion), Annals of Statistics, 22, 1701–1762.
37. WAKEFIELD, J. C., A. F. M. SMITH, A. Racine POON, and A. E. GELFAND (1994), Bayesian analysis of linear and non-linear population models by using the Gibbs sampler, Applied Statistics, 43, 201–221.
38. ZELLNER, A (1975), Bayesian analysis of regression error terms, Journal of the American Statistical Association, 70, 138–144.