We present a simple way to estimate the effects of changes in a vector of observable variables Xon 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.
Authors
Research Associate University of Arizona, University of Tokyo
Hidehiko is a Professor of Economics at the Eller College of Management, University of Arizona and a Research Associate at the IFS.
Reader in Econometrics London School of Economics and Political Science
Joseph Altonji
Journal article details
- DOI
- 10.3982/ECTA8004
- Publisher
- Wiley Online Library
- Issue
- Volume 80, Issue 4, July 2012
Suggested citation
J, Altonji and H, Ichimura and T, Otsu. (2012). 'Estimating derivatives in nonseparable models with limited dependent variables' 80(4/2012)
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