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

Chang Ching Lin; Shinichi Sataka

doi:10.1007/978-1-4614-1653-1_15

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

Chang Ching Lin, Shinichi Sataka

經濟學系

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	https://doi.org/10.1007/978-1-4614-1653-1_15
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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10.1007/978-1-4614-1653-1_15

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