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Local likelihood estimation of truncated regression and its partial derivatives: Theory and application

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Author Info
Park, Byeong U.
Simar, Léopold
Zelenyuk, Valentin

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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 well 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 should 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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File URL: http://www.sciencedirect.com/science/article/B6VC0-4T9CD04-1/2/ab09f5f8f0faae6c89185bcb9971a022
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Publisher Info
Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 146 (2008)
Issue (Month): 1 (September)
Pages: 185-198
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Handle: RePEc:eee:econom:v:146:y:2008:i:1:p:185-198

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Nonparametric truncated regression Local likelihood;

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Valentin Zelenyuk & Vitaliy Zheka, 2006. "Corporate Governance and Firm’s Efficiency: The Case of a Transitional Country, Ukraine," Journal of Productivity Analysis, Springer, vol. 25(1), pages 143-157, 04. [Downloadable!] (restricted)
  2. Simar, Leopold & Wilson, Paul W., 2007. "Estimation and inference in two-stage, semi-parametric models of production processes," Journal of Econometrics, Elsevier, vol. 136(1), pages 31-64, January. [Downloadable!] (restricted)
  3. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August. [Downloadable!] (restricted)
  4. Arthur Lewbel & Oliver Linton, 2002. "Nonparametric Censored and Truncated Regression," Econometrica, Econometric Society, vol. 70(2), pages 765-779, March. [Downloadable!] (restricted)
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  5. Kumbhakar, Subal C. & Park, Byeong U. & Simar, Leopold & Tsionas, Efthymios G., 2007. "Nonparametric stochastic frontiers: A local maximum likelihood approach," Journal of Econometrics, Elsevier, vol. 137(1), pages 1-27, March. [Downloadable!] (restricted)
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Cited by:
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  1. Luiza Badin & Cinzia Daraio & Léopold Simar, 2008. "Optimal Bandwidth Selection for Conditional Efficiency Measures: a Data-driven Approach," LEM Papers Series 2008/22, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy. [Downloadable!]
  2. Pavlo Demchuk & Valentin Zelenyuk, 2009. "Testing differences in efficiency of regions within a country: the case of Ukraine," Journal of Productivity Analysis, Springer, vol. 32(2), pages 81-102, October. [Downloadable!] (restricted)
  3. Leopold Simar & Valentin Zelenyuk, 2008. "Stochastic FDH/DEA estimators for Frontier Analysis," Discussion Papers 8, Kyiv School of Economics. [Downloadable!]
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