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Local Likelihood Estimation of Truncated Regression and Its Partial Derivatives: Theory and Application

Byeong U. Park, Leopold Simar and Valentin Zelenyuk
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Byeong U. Park: Seoul National University

No 7, Discussion Papers from Kyiv School of Economics

Abstract: In this paper we propose a very flexible estimator in the context of truncated regression that does not require parametric assumptions. To do this, we adapt the theory of local maximum likelihood estimation. We provide the asymptotic results and illustrate the performance of our estimator on simulated and real data sets. Our estimator performs as good as the fully parametric estimator when the assumptions for the latter hold, but as expected, much better when they do not (provided that the curse of dimensionality problem is not the issue). Overall, our estimator exhibits a fair degree of robustness to various deviations from linearity in the regression equation and also to deviations from the specification of the error term. So the approach shall prove to be very useful in practical applications, where the parametric form of the regression or of the distribution is rarely known.

Keywords: Nonparametric Truncated Regression; Local Likelihood (search for similar items in EconPapers)
JEL-codes: C14 C24 (search for similar items in EconPapers)
Date: 2008-05
New Economics Papers: this item is included in nep-ecm and nep-ore
Note: Published in Journal of Econometrics, 146, 185-198 (2008)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (44)

Downloads: (external link) First version, March 2006 (application/pdf)

Related works:
Journal Article: Local likelihood estimation of truncated regression and its partial derivatives: Theory and application (2008) Downloads
Working Paper: Local likelihood estimation of truncated regression and its partial derivatives: theory and application (2006) Downloads
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