Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171904
Authors: 
Heberle, Jochen
Sattarhoff, Cristina
Year of Publication: 
2017
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 5 [Year:] 2017 [Issue:] 1 [Pages:] 1-16
Abstract: 
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.
Subjects: 
GMM
HAC estimation
Newey-West estimator
Toeplitz matrices
discrete Fourier transformation (DFT)
R
JEL: 
C01
C55
C58
C63
G17
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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