Semiparametric estimation of moment condition models with weakly dependent data

Francesco Bravo, Ba Chu and David Jacho-Chávez

MPRA Paper from University Library of Munich, Germany

Abstract: This paper develops the asymptotic theory for the estimation of smooth semiparametric generalized estimating equations models with weakly dependent data. The paper proposes new estimation methods based on smoothed two-step versions of the generalised method of moments and generalised empirical likelihood methods. An important aspect of the paper is that it allows the first-step estimation to have an effect on the asymptotic variances of the second-step estimators and explicitly characterises this effect for the empirically relevant case of the so-called generated regressors. The results of the paper are illustrated with a partially linear model that has not been previously considered in the literature. The proofs of the results utilise a new uniform strong law of large numbers and a new central limit theorem for U-statistics with varying kernels that are of independent interest.

Keywords: Alpha-mixing; empirical processes; empirical likelihood; stochastic equicontinuity; uniform law of large numbers (search for similar items in EconPapers)
JEL-codes: C13 C14 (search for similar items in EconPapers)
Date: 2013, Revised 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Published in Journal of Nonparametric Statistics 1.29(2017): pp. 108-136

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Related works:
Journal Article: Semiparametric estimation of moment condition models with weakly dependent data (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:79686

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