Estimating Derivatives in Nonseparable Models with Limited Dependent Variables

Joseph Altonji, Hidehiko Ichimura and Taisuke Otsu

No 14161, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C1 C14 C23 C24 (search for similar items in EconPapers)
Date: 2008-07
Note: TWP
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Published as Joseph G. Altonji & Hidehiko Ichimura & Taisuke Otsu, 2012. "Estimating Derivatives in Nonseparable Models With Limited Dependent Variables," Econometrica, Econometric Society, vol. 80(4), pages 1701-1719, 07.

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Related works:
Journal Article: Estimating Derivatives in Nonseparable Models With Limited Dependent Variables (2012)
Working Paper: Estimating Derivatives in Nonseparable Models with Limited Dependent Variables (2011)
Working Paper: Estimating Derivatives in Nonseparable Models with Limited Dependent Variables (2008)
Working Paper: Estimating derivatives in nonseparable models with limited dependent variables (2008)
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