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

SUR 토빗회귀모형에서 베이지안 추정과 최대가능도 추정의 비교

Lee, Seung-Chun;Choi, Byongsu
이승천;최병수

  • Received : 2014.08.13
  • Accepted : 2014.10.07
  • Published : 2014.12.31

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

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Cited by

  1. The restricted maximum likelihood estimation of a censored regression model vol.24, pp.3, 2017, https://doi.org/10.5351/CSAM.2017.24.3.291

Acknowledgement

Supported by : 한신대학교, 한성대학교