AUTOMATIC LAG SELECTION IN COVARIANCE-MATRIX ESTIMATION
- 30 September 1994
- journal article
- research article
- Published by Oxford University Press (OUP) in The Review of Economic Studies
- Vol. 61 (4), 631-653
- https://doi.org/10.2307/2297912
Abstract
We propose a nonparametric method for automatically selecting the number of autocovariances to use in computing a heteroskedasticity and autocorrelation consistent covariance matrix. For a given kernel for weighting the autocovariances, we prove that our procedure is asymptotically equivalent to one that is optimal under a mean-squared error loss function. Monte Carlo simulations suggest that our procedure performs tolerably well, although it does result in size distortions.This publication has 2 references indexed in Scilit:
- Estimation of the Covariance Matrix of the Least-Squares Regression Coefficients When the Disturbance Covariance Matrix Is of Unknown FormEconometric Theory, 1991
- Consistent Autoregressive Spectral EstimatesThe Annals of Statistics, 1974