Density deconvolution with Laplace errors and unknown variance
Jun Cai (),
William Horrace () and
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Jun Cai: Huazhong University of Science and Technology
Journal of Productivity Analysis, 2021, vol. 56, issue 2, No 3, 103-113
Abstract We consider density deconvolution with zero-mean Laplace noise in the context of an error component regression model. We adapt the minimax deconvolution methods of Meister (2006) to allow estimation of the unknown noise variance. We propose a semi-uniformly consistent estimator for an ordinary-smooth target density and a modified "variance truncation device” for the unknown noise variance. We provide a simulation study and practical guidance for the choice of smoothness parameters of the ordinary-smooth target density. We apply restricted versions of our estimator to a stochastic frontier model of US banks and to a measurement error model of daily saturated fat intake.
Keywords: Stochastic frontier; Semi-parametric; Ordinary smooth (search for similar items in EconPapers)
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Working Paper: Density Deconvolution with Laplace Errors and Unknown Variance (2020)
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