Estimating Derivatives in Nonseparable Models with Limited Dependent Variables
Joseph G. Altonji
Yale University - Economic Growth Center; National Bureau of Economic Research (NBER)
Yale University - Cowles Foundation
Graduate School of Economics, University of Tokyo
July 15, 2008
Cowles Foundation Discussion Paper No. 1668
We present a simple way to estimate the effects of changes in a vector of observable variables X on a limited dependent variable Y when Y is a general nonseparable function of X and unobservables. We treat models in which Y is censored from above or below or potentially from both. The basic idea is to first estimate the derivative of the conditional mean of Y given X at x with respect to x on the uncensored sample without correcting for the effect of changes in x induced on the censored population. We then correct the derivative for the effects of the selection bias. We propose nonparametric and semiparametric estimators for the derivative. As extensions, we discuss the cases of discrete regressors, measurement error in dependent variables, and endogenous regressors in a cross section and panel data context.
Number of Pages in PDF File: 42
Keywords: Censored regression, Nonseparable models, Endogenous regressors, Tobit, Extreme quantiles
JEL Classification: C1, C14, C23, C24
Date posted: July 15, 2008 ; Last revised: July 20, 2008
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