A fast algorithm for the computation of HAC covariance matrix estimators - Jochen Heberle; Cristina Sattarhoff (Econometrics, 2017)
This paper considers the algorithmic implementation of the heteroskedasticity and autocorrelation consistent (HAC) estimation problem for covariance matrices of parameter estimators. We introduce a new algorithm, mainly based on the fast Fourier transform, and show via computer simulation that our algorithm is up to 20 times faster than well-established alternative algorithms. The cumulative effect is substantial if the HAC estimation problem has to be solved repeatedly. Moreover, the bandwidth parameter has no impact on this performance. We provide a general description of the new algorithm as well as code for a reference implementation in R.
Keywords: C01; C55; C58; C63; G17; 330; GMM; HAC estimation; Newey-West estimator; Toeplitz matrices; discrete Fourier transformation (DFT); R