Estimating Derivatives in Nonseparable Models with Limited Dependent Variables
Joseph G. Altonji
Yale University - Economic Growth Center; National Bureau of Economic Research (NBER)
University College London - Department of Economics
Yale University - Cowles Foundation
Cowles Foundation Discussion Paper No. 1668R
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, and X is independent of the unobservables. We treat models in which Y is censored from above, below, or 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 x on the censored population. We then correct the derivative for the effects of the selection bias. We discuss nonparametric and semiparametric estimators for the derivative. We also discuss the cases of discrete regressors and of endogenous regressors in both cross section and panel data contexts.
Number of Pages in PDF File: 21
Keywords: Censored regression, Nonseparable models, Endogenous regressors, Tobit, Extreme quantiles
JEL Classification: C1, C14, C23, C24working papers series
Date posted: May 13, 2011
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