Wavelet-based Estimation of Heteroskedasticity and Autocorrelation Consistent Covariance Matrices

As is well-known, a heteroskedasticity and autocorrelation consistent covariance matrix is proportional to a spectral density matrix at frequency zero and can be consistently estimated by such popular kernel methods as those of Andrews-Newey-West. In practice, it is di¢cult to estimate the spectral density matrix if it has a peak at frequency zero, which can arise when there is strong autocorrelation, as often encountered in economic and ...nancial time series. Kernels, as a local averaging method, tend to underestimate the peak, thus leading to strong overrejection in testing and overly narrow con...dence intervals in estimation. As a new mathematical tool generalizing Fourier transform, wavelet transform is a powerful tool to investigate such local properties as peaks and spikes, and thus is suitable for estimating covariance matrices. In this paper, we propose a class of wavelet estimators for the covariance matrices of econometric parameter estimators. We show the consistency of the wavelet-based covariance estimators and derive their asymptotic mean squared errors, which provide insight into the smoothing nature of wavelet estimation. We propose a data-driven method to select the ...nest scale—the smoothing parameter in wavelet estimation, making the wavelet estimators operational in practice. A simulation study compares the ...nite sample performances of the wavelet estimators and the kernel counterparts. As expected, the wavelet method outperforms the kernel method when there exists relatively strong autocorrelation in the data.