Qualitative and Asymptotic Performance of SNP Density Estimators
AbstractThe SNP estimator is the most convenient nonparametric method for simultaneously estimating the parameters of a nonlinear model and the density of a latent process by maximum likelihood. To determine if this convenience comes at a price, we assess the qualitative behavior of SNP in finite samples using the Marron--Wand test suite and verify theoretical convergence rates by Monte Carlo simulation. Our results suggest that there is no price for convenience because the SNP estimator is both qualitatively and asymptotically similar to the kernel estimator which is optimal.
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Bibliographic InfoPaper provided by Duke University, Department of Economics in its series Working Papers with number 96-17.
Date of creation: 1996
Date of revision:
Publication status: Published in JOURNAL OF ECONOMETRICS, Vol. 74, 1996, pages 77-118
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Other versions of this item:
- Fenton, Victor M. & Gallant, A. Ronald, 1996. "Qualitative and asymptotic performance of SNP density estimators," Journal of Econometrics, Elsevier, vol. 74(1), pages 77-118, September.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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