Automatic Lag Selection in Covariance Matrix Estimation
NBER Technical Working Paper No. 144
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.
Document Object Identifier (DOI): 10.3386/t0144
Users who downloaded this paper also downloaded these: