We generalize the Tobit censored regression to permit unique unobserved censoring thresholds conditioned by covariates and a set of common response coefficients. This situation , we argue, is one arising frequently in applications of censored regression and we provide three diverse examples to motivate the theory. We derive a robust estimation algorithm with three noteworthy features. First, by augmenting the observed-data likelihood with the censored observations, the estimation strategy is the same as Chib (1992) who derives Bayes estimates of the conventional censored regression. Second, by virtue of its generality, the model is applicable to a much broader set of circumstances than the conventional Tobit regression, which is nested as a special case of the more general framework. Third, despite its generality and wide app licability, the estimation algorithm is very simple, evidencing routine application of Markov chain Monte Carlo methods (MCMC)ÂGibbs sampling in particularÂand requiring only modest extensions of the basic algorithm in Chib (1992). The model and procedures are illustrated empirically in three applications that we use to motivate the theory, namely problems in transactions-costs economics, household decision-making and food-consumption.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.