SEMIPARAMETRIC ESTIMATION OF A PARTIALLY LINEAR CENSORED REGRESSION MODEL

Songnian Chen and Shakeeb Khan

Econometric Theory, 2001, vol. 17, issue 3, 567-590

Abstract: In this paper we propose an estimation procedure for a censored regression model where the latent regression function has a partially linear form. Based on a conditional quantile restriction, we estimate the model by a two stage procedure. The first stage nonparametrically estimates the conditional quantile function at in-sample and appropriate out-of-sample points, and the second stage involves a simple weighted least squares procedure. The proposed procedure is shown to have desirable asymptotic properties under regularity conditions that are standard in the literature. A small scale simulation study indicates that the estimator performs well in moderately sized samples.

Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:cup:etheor:v:17:y:2001:i:03:p:567-590_17

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