A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators

Jochen Heberle and Cristina Sattarhoff
Additional contact information
Jochen Heberle: Faculty of Business Administration, Universität Hamburg, 20146 Hamburg, Germany
Cristina Sattarhoff: Faculty of Business Administration, Universität Hamburg, 20146 Hamburg, Germany

Econometrics, 2017, vol. 5, issue 1, 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 .

Keywords: GMM; HAC estimation; Newey-West estimator; Toeplitz matrices; discrete Fourier transformation (DFT); R (search for similar items in EconPapers)
JEL-codes: B23 C C00 C01 C1 C2 C3 C4 C5 C8 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

Downloads: (external link)
https://www.mdpi.com/2225-1146/5/1/9/pdf (application/pdf)
https://www.mdpi.com/2225-1146/5/1/9/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jecnmx:v:5:y:2017:i:1:p:9-:d:88731

Access Statistics for this article

Econometrics is currently edited by Ms. Jasmine Liu

More articles in Econometrics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().