Advanced SearchSearch Tips
Bayesian Inference for Censored Panel Regression Model
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Bayesian Inference for Censored Panel Regression Model
Lee, Seung-Chun; Choi, Byongsu;
  PDF(new window)
It was recognized by some researchers that the disturbance variance in a censored regression model is frequently underestimated by the maximum likelihood method. This underestimation has implications for the estimation of marginal effects and asymptotic standard errors. For instance, the actual coverage probability of the confidence interval based on a maximum likelihood estimate can be significantly smaller than the nominal confidence level; consequently, a Bayesian estimation is considered to overcome this difficulty. The behaviors of the maximum likelihood and Bayesian estimators of disturbance variance are examined in a fixed effects panel regression model with a limited dependent variable, which is known to have the incidental parameter problem. Behavior under random effect assumption is also investigated.
Censored panel regression;Gibbs sampling;incidental parameter problem;
 Cited by
SUR 토빗회귀모형에서 베이지안 추정과 최대가능도 추정의 비교,이승천;최병수;

응용통계연구, 2014. vol.27. 6, pp.991-1002 crossref(new window)
A Bayesian inference for fixed effect panel probit model, Communications for Statistical Applications and Methods, 2016, 23, 2, 179  crossref(new windwow)
Amemiya, T. (1984). Tobit models: A survey, Journal of Econometrics, 24, 3-61. crossref(new window)

Bruno, G. (2004). Limited dependent panel models: A comparative analysis of classical and Bayesian inference among econometrics packages, Computing in Economics and Finance, Society for Computational Economics, name=SCE2004%&paper id=41.

Chib, S. (1992). Bayesian inference in the Tobit censored regression model, Journal of Econometrics, 51, 77-99.

Cowles, M. K., Carlin, B. P. and Connett, J. E. (1996). Bayesian Tobit modeling of longitudinal ordi-nal clinical trial compliance data with nonignorable missingness, Journal of American Statistical Association, 91, 86-98. crossref(new window)

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B. and Vehtari, A. (2013). Bayesian Data Analysis, 3rd edition, Chapman & Hall/CRC, New York.

Green W. (2004a). The behavior of the maximum likelihood estimator of limited dependent variable models in the presence of fixed effects, Econometrics Journal, 7, 98-119. crossref(new window)

GreenW. (2004b). Fixed effects and bias due to the incidental parameters problem in the Tobit model, Econometrics Reviews, 23, 125-147. crossref(new window)

Hamilton, B. H. (1999). HMO selection and medicare costs: Bayesian MCMC estimation of a robust panel data Tobit model with survival, Health Economics and Econometrics, 8, 403-414. crossref(new window)

Henningsen, A. (2011). censReg: Censored Regression (Tobit) Models, R package version 0.5,

Hobert, J. P. and Casella, G. (1996). The effect of improper priors on Gibbs sampling in hierarchical linear mixed models, Journal of the American Statistical Association, 91, 1461-1473. crossref(new window)

Kass, R. E. and Natarajan, R. (2006). A default conjugate prior for variance components in gener-alized linear mixed models (Comment on article by Browne and Draper), Bayesian Analysis, 1, 535-542. crossref(new window)

Kleiber, C. and Zeileis, A. (2009). AER: Applied Econometrics with R, R package version 1.1,

Lee, S.-C. and Choi, B. (2013). Bayesian interval estimation of Tobit regression model, The Korean Journal of Applied Statistics, 26, 737-746. crossref(new window)

Maddala, G. S. (1983). Limited-Dependent and Qualitative Variables in Econometrics, Cambridge University Press, New York.

Martin, A. D., Quinn, K. M. and Park, J. H. (2013). Markov chain Monte Carlo (MCMC) Package, R package version 1.3-3,

McCulloch, C. E. (1996). Fixed and random effects and best prediction, In Proceedings of the Kansas State Conference on Applied Statistics in Agriculture.

Morawetz, U. (2006). Bayesian modelling of panel data with individual effects applied to simulated data, Institut fur Nachhaltige Wirtschaftsentwicklung.

Tanner, M. A. and Wong, W.-H. (1987). The calculation of posterior distributions by data augmentation (with discussion), Journal of the American Statistical Association, 82, 528-550. crossref(new window)

Tobin, J. (1958). Estimation of relationships for limited dependent variables, Econometrica, 26, 24-36. crossref(new window)

Zeger, S. L. and Karim, M. R. (1991). Generalized linear models with random effects: A Gibbs sampling approach, Journal of the American Statistical Association, 86, 79-86. crossref(new window)

Lancaster, T. (2000). The incidental parameter problem since 1948, Journal of Econometrics, 95, 391-413. crossref(new window)

Lee, L. (2013). Nondetects And Data Analysis for Environmental Data, R pacage version 1.5-6,