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Bayesian Inference for Censored Panel Regression Model
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 Title & Authors
Bayesian Inference for Censored Panel Regression Model
Lee, Seung-Chun; Choi, Byongsu;
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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;

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