Working Paper: NBER ID: w14161
Authors: Joseph G. Altonji; Hidehiko Ichimura; Taisuke Otsu
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.
Keywords: nonseparable models; limited dependent variables; estimation; selection bias
JEL Codes: C1; C14; C23; C24
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| vector of observable variables (x) (C29) | limited dependent variable (y) (C29) |
| selection bias (C24) | biased estimates of the effect of x on y (C51) |
| correction for selection bias (C83) | unbiased estimates of the effect of x on y (C51) |
| measurement error and endogenous regressors (C20) | complications in estimating causal effects (C20) |