Next Article in Journal
Endogeneity, Time-Varying Coefficients, and Incorrect vs. Correct Ways of Specifying the Error Terms of Econometric Models
Previous Article in Journal
Between Institutions and Global Forces: Norwegian Wage Formation Since Industrialisation
Article Menu

Export Article

Open AccessArticle
Econometrics 2017, 5(1), 9; doi:10.3390/econometrics5010009

A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators

Faculty of Business Administration, Universität Hamburg, 20146 Hamburg, Germany
In memoriam Kostas Kyriakoulis.
*
Author to whom correspondence should be addressed.
Academic Editor: Marc S. Paolella
Received: 10 August 2016 / Revised: 5 January 2017 / Accepted: 9 January 2017 / Published: 25 January 2017
View Full-Text   |   Download PDF [311 KB, uploaded 25 January 2017]   |  

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. View Full-Text
Keywords: GMM; HAC estimation; Newey-West estimator; Toeplitz matrices; discrete Fourier transformation (DFT); R GMM; HAC estimation; Newey-West estimator; Toeplitz matrices; discrete Fourier transformation (DFT); R
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Heberle, J.; Sattarhoff, C. A Fast Algorithm for the Computation of HAC Covariance Matrix Estimators. Econometrics 2017, 5, 9.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Econometrics EISSN 2225-1146 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top