Estimation of Semiparametric Models when the Criterion Function is not Smooth

Xiaohong Chen (), Oliver Linton and Ingrid Van Keilegom ()

STICERD - Econometrics Paper Series from Suntory and Toyota International Centres for Economics and Related Disciplines, LSE

Abstract: 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.

Keywords: Empirical processes; non-smooth criterion; semiparametric estimation; stochastic equicontinuity. (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2003-05
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Citations: View citations in EconPapers (340)

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https://sticerd.lse.ac.uk/dps/em/em450.pdf (application/pdf)

Related works:
Journal Article: Estimation of Semiparametric Models when the Criterion Function Is Not Smooth (2003)
Working Paper: Estimation of semiparametric models when the criterion function is not smooth (2003)
Working Paper: Estimation of semiparametric models when the criterion function is not smooth (2002)
Working Paper: Estimation of semiparametric models when the criterion function is not smooth (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:cep:stiecm:450

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