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dc.contributor.authorLi, Lexin-
dc.contributor.authorSimonoff, Jeffrey S.-
dc.contributor.authorTsai, Chih-Ling-
dc.date.accessioned2008-05-25T13:50:24Z-
dc.date.available2008-05-25T13:50:24Z-
dc.date.issued2006-
dc.identifier.urihttp://hdl.handle.net/2451/26301-
dc.description.abstractIt is not unusual for the response variable in a regression model to be subject to censoring or truncation. Tobit regression models are a specific example of such a situation, where for some observations the observed response is not the actual response, but rather the censoring value (often zero), and an indicator that censoring (from below) has occurred. It is well-known that the maximum likelihood estimator for such a linear model (assuming Gaussian errors) is not consistent if the error term is not homoscedastic and normally distributed. In this paper we consider estimation in the Tobit regression context when those conditions do not hold, as well as when the true response is an unspecified nonlinear function of linear terms, using sliced inverse regression (SIR). The properties of SIR estimation for Tobit models are explored both theoretically and based on Monte Carlo simulations. It is shown that the SIR estimator has good properties when the usual linear model assumptions hold, and can be much more effective than other estimators when they do not. An example related to household charitable donations demonstrates the usefulness of the estimator.en
dc.languageEnglishEN
dc.language.isoen_USen
dc.publisherStern School of Business, New York Universityen
dc.relation.ispartofseriesSOR-2006-1en
dc.subjectDimension reductionen
dc.subjectHeteroscedasticityen
dc.subjectNonnormalityen
dc.subjectSingle-index modelen
dc.titleTobit Model Estimation and Sliced Inverse Regressionen
dc.typeWorking Paperen
dc.description.seriesStatistics Working Papers SeriesEN
Appears in Collections:IOMS: Statistics Working Papers

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