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

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  • Park, Byeong
  • Simar, Leopold
  • Zelenyuk, Valentin

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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 34686.

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Date of creation: 19 Mar 2006
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Publication status: Published in Journal of Econometrics 146.1(2008): pp. 185-198
Handle: RePEc:pra:mprapa:34686

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Keywords: Nonparametric Truncated Regression; Local Likelihood;

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References

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  1. Subodh Kumar & R. Robert Russell, 2002. "Technological Change, Technological Catch-up, and Capital Deepening: Relative Contributions to Growth and Convergence," American Economic Review, American Economic Association, vol. 92(3), pages 527-548, June.
  2. Arthur Lewbel & Oliver Linton, 2000. "Nonparametric censored and truncated regression," LSE Research Online Documents on Economics 2060, London School of Economics and Political Science, LSE Library.
  3. 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.
  4. Vives,Xavier (ed.), 2006. "Corporate Governance," Cambridge Books, Cambridge University Press, number 9780521032032, November.
  5. 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.
  6. S. Illeris & G. Akehurst, 2001. "Introduction," The Service Industries Journal, Taylor & Francis Journals, vol. 21(1), pages 1-4, January.
  7. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
  8. 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.
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Citations

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Cited by:
  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.
  2. Claudia Curi & Paolo Guarda & Ana Lozano-Vivas & Valentin Zelenyuk, 2011. "Is foreign-bank efficiency in financial centers driven by homecountry characteristics?," BCL working papers 68, Central Bank of Luxembourg.
  3. Grigol, Modebadze, 2011. "Foreign Investment Effects on the Banking Sector in Georgia," MPRA Paper 32897, University Library of Munich, Germany.
  4. Léopold Simar & Paul Wilson, 2011. "Two-stage DEA: caveat emptor," Journal of Productivity Analysis, Springer, vol. 36(2), pages 205-218, October.
  5. Leopold Simar & Valentin Zelenyuk, 2008. "Stochastic FDH/DEA estimators for Frontier Analysis," Discussion Papers 8, Kyiv School of Economics.
  6. 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.
  7. Minegishi, Kota, 2013. "Explaining Production Heterogeneity By Contextual Environments: Two-Stage DEA Application to Technical Change Measurement," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150289, Agricultural and Applied Economics Association.
  8. Ding, Lan & Li, Haizheng, 2012. "Social networks and study abroad — The case of Chinese visiting students in the US," China Economic Review, Elsevier, vol. 23(3), pages 580-589.
  9. Halkos, George & Tzeremes, Nickolaos, 2012. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from the UK regions," MPRA Paper 38147, University Library of Munich, Germany.
  10. Shiu, Alice & Zelenyuk, Valentin, 2009. "Production Efficiency versus Ownership: The Case of China," MPRA Paper 23760, University Library of Munich, Germany, revised 22 Mar 2010.
  11. Halkos, George E. & Tzeremes, Nickolaos G., 2013. "A conditional directional distance function approach for measuring regional environmental efficiency: Evidence from UK regions," European Journal of Operational Research, Elsevier, vol. 227(1), pages 182-189.

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