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Title:Estimating derivatives in nonseparable models with limited dependent variables PDF Logo
Authors:Altonji, Joseph
Ichimura, Hidehiko
Otsu, Taisuke
Issue Date:2008
Series/Report no.:cemmap working paper CWP20/08
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.
Persistent Identifier of the first edition:doi:10.1920/wp.cem.2008.2008
Document Type:Working Paper
Appears in Collections:cemmap working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies (IFS)

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