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Estimation of Semiparametric Models when the Criterion Function is not Smooth Author info | Abstract | Publisher info | Download info | Related research | Statistics Xiaohong Chen
Oliver Linton
Ingrid Van Keilegom
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We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions and simultaneously depends on some nonparametric estimators that can themselves depend on the parameters to be estimated. Our results extend existing theories like those of Pakes and Pollard (1989), Andrews (1994a) and Newey (1994). We also show that bootstrap provides asymptotically correct confidence regions for the finite dimensional parameters. We apply our results to two examples: a 'hit rate' and a partially linear median regression with some endogenous regressors.
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Paper provided by Suntory and Toyota International Centres for Economics and Related Disciplines, LSE in its series STICERD - Econometrics Paper Series with number
/2003/450.
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Date of creation: May 2003Date of revision:
Handle: RePEc:cep:stiecm:/2003/450Contact details of provider: Web page: http://sticerd.lse.ac.uk/publications/
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Keywords: Empirical processes non-smooth criterion semiparametric estimation stochastic equicontinuity. Other versions of this item:
Find related papers by JEL classification: C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
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