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Weighted Minimum Mean-Square Distance from Independence Estimation

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Author Info
Donald J. Brown () (Cowles Foundation, Yale University)
Marten H. Wegkamp

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Abstract

In this paper we introduce a family of semi-parametric estimators, suggested by Manski's minimum mean-square distance from independence estimator. We establish the strong consistency, asymptotic normality and consistency of bootstrap estimates of the sampling distribution and the asymptotic variance of these estimators.

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File URL: http://cowles.econ.yale.edu/P/cd/d12b/d1288.pdf
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Publisher Info
Paper provided by Cowles Foundation, Yale University in its series Cowles Foundation Discussion Papers with number 1288.

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Length: 31 pages
Date of creation: Feb 2001
Date of revision:
Publication status: Published in Econometrica (September 2002), 70(5): 2035-2051
Handle: RePEc:cwl:cwldpp:1288

Note: CFP 1042
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Postal: Cowles Foundation, Yale University, Box 208281, New Haven, CT 06520-8281 USA

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Related research
Keywords: Semiparametric estimation; simultaneous equations models; empirical processes; extremum estimators;

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Find related papers by JEL classification:
C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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  1. Xiaohong Chen & Oliver Linton & Ingred Van Keilegom, 2002. "Estimation of semiparametric models when the criterion function is not smooth," CeMMAP working papers CWP02/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
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