Browse > Article
http://dx.doi.org/10.5351/KJAS.2014.27.6.991

A Comparison of Bayesian and Maximum Likelihood Estimations in a SUR Tobit Regression Model  

Lee, Seung-Chun (Department of Applied Statistics, Hanshin University)
Choi, Byongsu (Department of Multimedia Engineering, Hansung University)
Publication Information
The Korean Journal of Applied Statistics / v.27, no.6, 2014 , pp. 991-1002 More about this Journal
Abstract
Both Bayesian and maximum likelihood methods are efficient for the estimation of regression coefficients of various Tobit regression models (see. e.g. Chib, 1992; Greene, 1990; Lee and Choi, 2013); however, some researchers recognized that the maximum likelihood method tends to underestimate the disturbance variance, which has implications for the estimation of marginal effects and the asymptotic standard error of estimates. The underestimation of the maximum likelihood estimate in a seemingly unrelated Tobit regression model is examined. A Bayesian method based on an objective noninformative prior is shown to provide proper estimates of the disturbance variance as well as other regression parameters
Keywords
Seemingly unrelated Tobit regression model; maximum likelihood estimate; EM algorithm; Bayes estimation; Gibbs sampling;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 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, http://editorialexpress.com/cgi-bin/conference/ download.cgi?db name=SCE2004%&paper id=41.
2 Greene, W. (2004b). Fixed effects and bias due to the incidental parameters problem in the Tobit model, Econometrics Reviews, 23, 125-147.   DOI
3 Greene, W. (2012). Econometric Analysis. 7th edition, Pearson.
4 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.   DOI
5 Huang, C. J., Sloan, F. and Adamache, K, W. (1987). Estimation of seemingly unrelated Tobit regression via EM algorithm, Journal of Business & Economic Statistics, 5, 425-430.
6 Chib, S. (1992). Bayesian inference in the Tobit censored regression model, Journal of Econometrics, 51, 77-99.
7 Greene, W. H. (1990). Econometric Analysis, Macmillan, New York.
8 Cowles, M. K., Carlin, B. P. and Connett, J. E. (1996). Bayesian Tobit modeling of longitudinal ordinal clinical trial compliance data with nonignorable missingness, Journal of American Statistical Association, 91, 86-98.   DOI
9 Lancaster, T. (2000). The incidental parameter problem since 1948, Journal of Econometrics, 95, 391-413.   DOI
10 Johnson, S. G. and Narasimhan, B. (2014). Package Cubature, http://ab-initio.mit.edu/wiki/index.php/Cubature.
11 Lee, S.C. and Choi, B. (2013). Bayesian interval estimation of Tobit regression model, The Korean Journal of Applied Statistics, 26, 737-746.   과학기술학회마을   DOI
12 Lee, S.C. and Choi, B. (2014). Bayesian inference for censored panel regression model, Communications for Statistical Applications and Methods, 21, 192-200.
13 Amemiya, T. (1984). Tobit models: A survey, Journal of Econometrics, 24, 3-61   DOI   ScienceOn
14 Daniels, M, D. and Kass, R. E. (1999). Nonconjugate Bayesian estimation of covariance matrices and its use in hierarchical models, Journal of American Statistical Association, 94, 1254-1263.   DOI
15 Efron, B. and Hinkley, D. V. (1978). The observed versus expected information, Biometirika, 65, 163-168.
16 Greene, 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.   DOI
17 Tierney, L. and Kadane, J. B. (1986). Accurate approximations for posterior moments and marginal densities, Journal of American Statistical Association, 81, 82-86.   DOI   ScienceOn
18 Louis, T. A. (1982). Finding the observed information matrix when using the EM algorithm, Journal of the Royal Statistical Society: Series B, 44, 226-233.
19 Natarajan, R. and Kass, R. R. (2000). Bayesian methods for generalized linear mixed models, Journal of the American Statistical Association, 95, 227-237.   DOI
20 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.   DOI
21 Wichitaksorn, N. and Choy, S. T. B. (2011). Modeling dependence of seemingly unrelated Tobit model through copula: A Bayesian analysis, Thailand Econometrics Society, 3, 6-19.
22 Joreskog (2004). Multivariate censored regression, available at www.ssicentral.com/lisrel/column12.htm.
23 Huang, H. C. (1999). Estimation of the SUR Tobit model via the MCECM algorithm, Economics Letters, 64, 25-30.   DOI
24 Huang, H. C. (2001). Bayesian analysis of the SUR Tobit model, Applied Economics Letters, 8, 617-622.   DOI