Byeong U. Park (Seoul National University) Leopold Simar (Universite Catholique de Louvain and Toulouse School of Economics) Valentin Zelenyuk (Kyiv School of Economics and Kyiv Economics Institute)
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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.
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Paper provided by Kyiv School of Economics in its series Discussion Papers with number
7.
Length: Date of creation: May 2008 Date of revision: Handle: RePEc:kse:dpaper:7
Note: Published in Journal of Econometrics, 146, 185-198 (2008) Contact details of provider: Postal: 13 Yakira Str, 04119 Kyiv Phone: (38-044)492-8012 Fax: (38-044)492-8011 Email: Web page: http://www.kse.org.ua/ More information through EDIRC
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Find related papers by JEL classification: C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models
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