Z-estimators and auxiliary information for strong mixing processes

Federico Crudu, Emilio Porcu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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.

Original languageBritish English
Pages (from-to)1-11
Number of pages11
JournalStochastic Environmental Research and Risk Assessment
Volume33
Issue number1
DOIs
StatePublished - 15 Jan 2019

Keywords

  • Blocking techniques
  • Generalized empirical likelihood
  • GMM
  • M-estimators
  • Z-estimators
  • α-Mixing

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