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

Chang Ching Lin, Shinichi Sataka

研究成果: Chapter

摘要

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.

原文English
主出版物標題Recent Advances and Future Directions in Causality, Prediction, and Specification Analysis
主出版物子標題Essays in Honor of Halbert L. White Jr
發行者Springer New York
頁面383-410
頁數28
ISBN(電子)9781461416531
ISBN(列印)9781461416524
DOIs
出版狀態Published - 2013 一月 1

All Science Journal Classification (ASJC) codes

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

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