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.
All Science Journal Classification (ASJC) codes
- Economics, Econometrics and Finance(all)
- Business, Management and Accounting(all)