A Simple Nonparametric Approach to Estimating the Distribution of Random Coefficients in Structural Models
AbstractWe explore a nonparametric mixtures estimator for recovering the joint distribution of random coefficients in economic models. The estimator is based on linear regression subject to linear inequality constraints and is computationally attractive compared to alternative, nonparametric estimators. We provide conditions under which the estimated distribution function converges to the true distribution in the weak topology on the space of distributions. We verify the consistency conditions for discrete choice, continuous outcome and selection models.
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Bibliographic InfoPaper provided by National Bureau of Economic Research, Inc in its series NBER Working Papers with number 17283.
Date of creation: Aug 2011
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Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- L0 - Industrial Organization - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-08-22 (All new papers)
- NEP-DCM-2011-08-22 (Discrete Choice Models)
- NEP-ECM-2011-08-22 (Econometrics)
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