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