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Semiparametric deconvolution with unknown error variance

Author

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  • William Horrace

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  • Christopher Parmeter

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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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Suggested Citation

  • William Horrace & Christopher Parmeter, 2011. "Semiparametric deconvolution with unknown error variance," Journal of Productivity Analysis, Springer, vol. 35(2), pages 129-141, April.
  • Handle: RePEc:kap:jproda:v:35:y:2011:i:2:p:129-141
    DOI: 10.1007/s11123-010-0193-z
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    References listed on IDEAS

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    8. 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.
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    Citations

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    Cited by:

    1. William C. Horrace & Ian A. Wright, 2016. "Stationary Points for Parametric Stochastic Frontier Models," Center for Policy Research Working Papers 196, 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.
    3. Daouia, Abdelaati & Florens, Jean-Pierre & Simar, Léopold, 2016. "Robust frontier estimation from noisy data: a Tikhonov regularization approach," TSE Working Papers 16-665, Toulouse School of Economics (TSE), revised Feb 2018.
    4. repec:eee:ejores:v:263:y:2017:i:3:p:1078-1094 is not listed on IDEAS
    5. repec:eee:ejores:v:262:y:2017:i:2:p:792-801 is not listed on IDEAS
    6. 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.
    7. Dai, Xiaofeng, 2016. "Non-parametric efficiency estimation using Richardson–Lucy blind deconvolution," European Journal of Operational Research, Elsevier, vol. 248(2), pages 731-739.
    8. Fan Zhang & Joshua Hall & Feng Yao, 2017. "Does Economic Freedom Affect The Production Frontier? A Semiparametric Approach With Panel Data," Working Papers 17-27, Department of Economics, West Virginia University.

    More about this item

    Keywords

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

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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