TY  - JOUR
AU  - Haan,Wouter J. Den
AU  - Levin,Andrew
TI  - Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures
JF  - National Bureau of Economic Research Technical Working Paper Series
VL  - No. 195
PY  - 1996
Y2  - May 1996
UR  - http://www.nber.org/papers/t0195
L1  - http://www.nber.org/papers/t0195.pdf
N1  - Author contact info:
Andrew T.. Levin
Federal Reserve Board
Mail Stop 77
20th and C Street, NW
Washington, DC   20551
Tel: 202-452-3541
Fax: 202-452-2301
E-Mail: andrew.levin@frb.gov
AB  - In this paper, we propose a parametric spectral estimation procedure for constructing heteroskedasticity and autocorrelation consistent (HAC) covariance matrices.  We establish the consistency of this procedure under very general conditions similar to those considered in previous research, and we demonstrate that the parametric estimator converges at a faster rate than the kernel-based estimators proposed by Andrews and Monahan (1992) and Newey and West (1994).  In finite samples, our Monte Carlo experiments indicate that the parametric estimator matches, and in some cases greatly exceeds, the performance of the prewhitened kernel estimator proposed by Andrews and Monahan (1992).  These simulation experiments illustrate several important limitations of non-parametric HAC estimation procedures, and highlight the advantages of explicitly modeling the temporal properties of the error terms. Wouter J. den Haan                                   Andrew Levin Depa
ER  -