Local Likelihood Estimation of Truncated Regression and Its Partial Derivatives: Theory and Application
AbstractIn 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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Bibliographic InfoPaper provided by Kyiv School of Economics in its series Discussion Papers with number 7.
Date of creation: May 2008
Date of revision:
Note: Published in Journal of Econometrics, 146, 185-198 (2008)
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Nonparametric Truncated Regression; Local Likelihood;
Other versions of this item:
- Park, Byeong U. & Simar, Léopold & Zelenyuk, Valentin, 2008. "Local likelihood estimation of truncated regression and its partial derivatives: Theory and application," Journal of Econometrics, Elsevier, vol. 146(1), pages 185-198, September.
- Park, Byeong & Simar, Leopold & Zelenyuk, Valentin, 2006. "Local likelihood estimation of truncated regression and its partial derivatives: theory and application," MPRA Paper 34686, University Library of Munich, Germany.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-06-07 (All new papers)
- NEP-ECM-2008-06-07 (Econometrics)
- NEP-ORE-2008-06-07 (Operations Research)
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