Estimation of Semiparametric Models when the Criterion Function Is Not Smooth
Xiaohong Chen (),
Oliver Linton () and
Ingrid Van Keilegom ()
Econometrica, 2003, vol. 71, issue 5, 1591-1608
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 such as 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. Copyright The Econometric Society 2003.
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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 (2003)
Working Paper: Estimation of semiparametric models when the criterion function is not smooth (2002)
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