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Z-estimators and auxiliary information for strong mixing processes

Lookup NU author(s): Professor Emilio Porcu

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Abstract

© 2018, Springer-Verlag GmbH Germany, part of Springer Nature. This paper introduces a weighted Z-estimator for moment condition models, assuming auxiliary information on the unknown distribution of the data and under the assumption of weak dependence (strong mixing processes). We model serial dependence through a simple nonparametric blocking device, routinely used in the bootstrap literature. The weights that carry the auxiliary information are computed by means of generalized empirical likelihood. The resulting weighted estimator is shown to be consistent and asymptotically normal. The proposed estimator is computationally simple and shows nice finite sample features when compared to asymptotically equivalent estimators.

Publication metadata

Author(s): Crudu F, Porcu E

Publication type: Article

Publication status: Published

Journal: Stochastic Environmental Research and Risk Assessment

Year: 2019

Volume: 33

Pages: 1-11

Print publication date: 15/01/2019

Online publication date: 29/08/2018

Acceptance date: 02/04/2018

ISSN (print): 1436-3240

ISSN (electronic): 1436-3259

Publisher: Springer Nature

URL: https://doi.org/10.1007/s00477-018-1602-5

DOI: 10.1007/s00477-018-1602-5

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