Uniform Convergence of Weighted Sums of Non- and Semi-parametric Residuals for Estimation and Testing
Juan Carlos Escanciano (),
David Jacho-Chávez () and
Arthur Lewbel ()
No 756, Boston College Working Papers in Economics from Boston College Department of Economics
A new uniform expansion is introduced for sums of weighted kernel-based regression residuals from nonparametric or semiparametric models. This result is useful for deriving asymptotic properties of semiparametric estimators and test statistics with data-dependent bandwidth, random trimming, and estimated weights. An extension allows for generated regressors, without requiring the calculation of functional derivatives. Example applications are provided for a binary choice model with selection, including a new semiparametric maximum likelihood estimator, and a new directional test for correct specification of the average structural function. An extended Appendix contains general results on uniform rates for kernel estimators, additional applications, and primitive sufficient conditions for high level assumptions.
Keywords: Double index models; Two step estimators; Semiparametric regression; Control function estimators; Sample selection models; Empirical process theory; Limited dependent variables; Oracle estimators; Migration (search for similar items in EconPapers)
JEL-codes: C13 C14 C21 D24 (search for similar items in EconPapers)
Date: 2010-05-01, Revised 2012-01-31
New Economics Papers: this item is included in nep-ecm
Note: Previously circulated as "Uniform Convergence for Semiparametric Two Step Estimators and Tests"
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Journal Article: Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing (2014)
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