Tobit Model Estimation and Sliced Inverse Regression
affiliation not provided to SSRN
Jeffrey S. Simonoff
New York University (NYU) - Leonard N. Stern School of Business; New York University (NYU) - Department of Information, Operations, and Management Sciences
University of California, Davis - Graduate School of Management
NYU Working Paper No. SOR-2006-1
It 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 (oftenzero), 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 propertiesof 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, andcan be much more effective than other estimators when they do not. An example related to household charitable donations demonstrates the usefulness of the estimator.
Number of Pages in PDF File: 22
Keywords: Dimension reduction, Heteroscedasticity, Nonnormality, Single-index modelworking papers series
Date posted: November 3, 2008
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