On long-run covariance matrix estimation with the truncated flat kernel

Chang Ching Lin, Shinichi Sataka

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Despite its large sample efficiency, the truncated flat kernel (TF) estimator of long-run covariance matrices is seldom used, because it occasionally gives a nonpositive semidefinite estimate and sometimes performs poorly in small samples, compared to other familiar kernel estimators. This paper proposes simple modifications to the TF estimator to enforce the positive definiteness without sacrificing the large sample efficiency and make the estimator more reliable in small samples through better utilization of the bias-variance trade-off. We study the large sample properties of the modified TF estimators and verify their improved small-sample performances by Monte Carlo simulations.

Original languageEnglish
Title of host publicationRecent Advances and Future Directions in Causality, Prediction, and Specification Analysis
Subtitle of host publicationEssays in Honor of Halbert L. White Jr
PublisherSpringer New York
Pages383-410
Number of pages28
ISBN (Electronic)9781461416531
ISBN (Print)9781461416524
DOIs
Publication statusPublished - 2013 Jan 1

All Science Journal Classification (ASJC) codes

  • Economics, Econometrics and Finance(all)
  • Business, Management and Accounting(all)

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