Uniform convergence of weighted sums of non and semiparametric residuals for estimation and testing

A new uniform expansion is introduced for sums of weighted kernel-based regression residuals from nonparametric or semiparametric models. This expansion is useful for deriving asymptotic properties of semiparametric estimators and test statistics with data-dependent bandwidths, random trimming, and...

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Bibliographic Details
Published in:Journal of econometrics 2014-01, Vol.178 (3), p.426-443
Main Authors: Escanciano, Juan Carlos, Jacho-Chávez, David T., Lewbel, Arthur
Format: Article
Language:English
Subjects:
Asymptotic methods
Convergence
Decision making models
Economic theory
Empirical process theory
Estimating techniques
Estimation
Limited dependent variables
Nonparametric residuals
Parameter estimation
Regression analysis
Residuals
Sample selection models
Sampling
Semiparametric regression
Semiparametric residuals
Statistical analysis
Studies
Uniform-in-bandwidth
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Summary:A new uniform expansion is introduced for sums of weighted kernel-based regression residuals from nonparametric or semiparametric models. This expansion is useful for deriving asymptotic properties of semiparametric estimators and test statistics with data-dependent bandwidths, random trimming, and estimated efficiency weights. Provided examples include a new estimator for a binary choice model with selection and an associated directional test for specification of this model’s average structural function. An appendix contains new results on uniform rates for kernel estimators and primitive sufficient conditions for high level assumptions commonly used in semiparametric estimation.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2013.06.004