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Semiparametric deconvolution with unknown error variance

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  • William Horrace

    ()

  • Christopher Parmeter

    ()

Abstract

Deconvolution is a useful statistical technique for recovering an unknown density in the presence of measurement error. Typically, the method hinges on stringent assumptions about teh nature of the measurement error, more specifically, that the distribution is *entirely* known. We relax this assumption in the context of a regression error component model and develop an estimator for the unkinown density. We show semi-uniform consistency of the estimator and provide Monte Carlo evidence that demonstrates the merits of the method.

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Bibliographic Info

Article provided by Springer in its journal Journal of Productivity Analysis.

Volume (Year): 35 (2011)
Issue (Month): 2 (April)
Pages: 129-141

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Handle: RePEc:kap:jproda:v:35:y:2011:i:2:p:129-141

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Web page: http://www.springerlink.com/link.asp?id=100296

Related research

Keywords: Error component; Ordinary smooth; Semi-uniform consistency; C14; C21; D24;

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References

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  12. Wei Siang Wang & Peter Schmidt, 2007. "On The Distribution of Estimated Technical Efficiency in Stochastic Frontier Models," CEPA Working Papers Series WP022007, School of Economics, University of Queensland, Australia.
  13. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
  14. C. Lanier Benkard & Patrick Bajari, 2005. "Hedonic Price Indexes With Unobserved Product Characteristics, and Application to Personal Computers," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 61-75, January.
  15. A. Delaigle & I. Gijbels, 2004. "Bootstrap bandwidth selection in kernel density estimation from a contaminated sample," Annals of the Institute of Statistical Mathematics, Springer, vol. 56(1), pages 19-47, March.
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Citations

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Cited by:
  1. William C. Horrace & Christopher F. Parmeter, 2014. "A Laplace Stochastic Frontier Model," Center for Policy Research Working Papers 166, Center for Policy Research, Maxwell School, Syracuse University.
  2. Qu Feng & William Horrace & Guiying Laura Wu, 2013. "Wrong Skewness and Finite Sample Correction in Parametric Stochastic Frontier Models," Center for Policy Research Working Papers 154, Center for Policy Research, Maxwell School, Syracuse University.

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