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Ill-posed inverse problems in economics
Horowitz, Joel - 2013
A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter. Often, this mapping is obtained by inverting a mapping from the parameter to the population distribution. If the inverse...
Persistent link: https://www.econbiz.de/10010318682
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Density Deconvolution with Replicate Measurements and Auxiliary Data
McIntyre, Julie P. - 2003
We present two deconvolution estimators for the density function of a random variable X that is measured with error … estimator generalizes the deconvolution estimator of Stefanski and Carroll (1990), with the measurement error variances … study and an example.The second is a semi-parametric deconvolution estimator that assumes the availability of a covariate …
Persistent link: https://www.econbiz.de/10009431272
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Adaptive density estimation in deconvolution problems with unknown error distribution
Kappus, Johanna; Mabon, Gwennae͏̈lle - 2013
Persistent link: https://www.econbiz.de/10010342689
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Ill-posed inverse problems in economics
Horowitz, Joel - 2013
A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter. Often, this mapping is obtained by inverting a mapping from the parameter to the population distribution. If the inverse...
Persistent link: https://www.econbiz.de/10009778441
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Ill-Posed Inverse Problems in Economics
Horowitz, Joel L. - In: Annual Review of Economics 6 (2014) 1, pp. 21-51
A parameter of an econometric model is identified if there is a one-to-one or many-to-one mapping from the population distribution of the available data to the parameter. Often, this mapping is obtained by inverting a mapping from the parameter to the population distribution. If the inverse...
Persistent link: https://www.econbiz.de/10010886200
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Consistent Density Deconvolution under Partially Known Error Distribution
Schwarz, Maik; Van Bellegem, Sébastien - Toulouse School of Economics (TSE) - 2009
We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identifiable from the observations if we restrict the class of considered distributions by a...
Persistent link: https://www.econbiz.de/10008465315
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Optimal bandwidth selection for multivariate kernel deconvolution density estimation
Youndjé, Élie; Wells, Martin - In: TEST: An Official Journal of the Spanish Society of … 17 (2008) 1, pp. 138-162
Persistent link: https://www.econbiz.de/10005759574
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Consistent Density Deconvolution under Partially Known Error Distribution
Schwarz, Maik; Van Bellegem, Sébastien - Institut d'Économie Industrielle (IDEI), Toulouse … - 2009
We estimate the distribution of a real-valued random variable from contaminated observations. The additive error is supposed to be normally distributed, but with unknown variance. The distribution is identifiable from the observations if we restrict the class of considered distributions by a...
Persistent link: https://www.econbiz.de/10008492575
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A Simple Deconvolving Kernel Density Estimator when Noise is Gaussian
Proenca, Isabel - EconWPA - 2005
Deconvolving kernel estimators when noise is Gaussian entail heavy calculations. In order to obtain the density estimates numerical evaluation of a specific integral is needed. This work proposes an approximation to the deconvolving kernel which simplifies considerably calculations by avoiding...
Persistent link: https://www.econbiz.de/10005062537
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Iterative Density Estimation from Contaminated Observations
Hesse, Christian - In: Metrika 64 (2006) 2, pp. 151-165
Persistent link: https://www.econbiz.de/10005598719