<p>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 <i>x</i> with respect to <i>x</i> on the uncensored sample without correcting for the effect of changes in <i>x</i> 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. </p>
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
Working Paper details
- DOI
- 10.1920/wp.cem.2008.2008
- Publisher
- IFS
Suggested citation
J, Altonji and H, Ichimura and T, Otsu. (2008). Estimating derivatives in nonseparable models with limited dependent variables. London: IFS. Available at: https://ifs.org.uk/publications/estimating-derivatives-nonseparable-models-limited-dependent-variables (accessed: 13 May 2024).
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