Calzolari, Giorgio and Fiorentini, Gabriele (1993): Estimating variances and covariances in a censored regression model. Published in: Statistica No. 53 (1993): pp. 323-339.
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When the coefficients of a Tobit model are estimated by maximum likelihood their covariance matrix is typically, even if not necessarily, associated with the algorithm employed to maximize the likelihood. Covariance estimators used in practice are derived by: (1) the Hessian (observed information), (2) the matrix of outer products of the first derivatives of the log-likelihood (OPG version), (3) the expected Hessian (estimated information), (4) a mixture of 1 and 2 (White's QML covariance matrix). Significant differences among these estirnates are are usually interpreted as an indication of misspecification. From our Monte Carlo study this seems not to be true, unless the sample size is really very large. Even in absence of misspecification, large differences are encountered in small samples, and the sign of the differences is almost systematic. This suggests that the choice of the covariance estimator is not neutral and the results of hypotheses testing may be strongly affected by such a choice.
|Item Type:||MPRA Paper|
|Original Title:||Estimating variances and covariances in a censored regression model|
|Keywords:||Tobit model, maximum likelihood, hessian matrix, outer products matrix, covariance estimators|
|Subjects:||C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General
C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models; Multiple Variables > C34 - Truncated and Censored Models; Switching Regression Models
C - Mathematical and Quantitative Methods > C2 - Single Equation Models; Single Variables > C24 - Truncated and Censored Models; Switching Regression Models
|Depositing User:||Giorgio Calzolari|
|Date Deposited:||10. May 2010 12:53|
|Last Modified:||18. Feb 2013 00:31|
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