Artikel
A fast algorithm for the computation of HAC covariance matrix estimators
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.
- Sprache
-
Englisch
- Erschienen in
-
Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 5 ; Year: 2017 ; Issue: 1 ; Pages: 1-16 ; Basel: MDPI
- Klassifikation
-
Wirtschaft
Econometrics
Large Data Sets: Modeling and Analysis
Financial Econometrics
Computational Techniques; Simulation Modeling
Financial Forecasting and Simulation
- Thema
-
GMM
HAC estimation
Newey-West estimator
Toeplitz matrices
discrete Fourier transformation (DFT)
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Heberle, Jochen
Sattarhoff, Cristina
- Ereignis
-
Veröffentlichung
- (wer)
-
MDPI
- (wo)
-
Basel
- (wann)
-
2017
- DOI
-
doi:10.3390/econometrics5010009
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:45 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Heberle, Jochen
- Sattarhoff, Cristina
- MDPI
Entstanden
- 2017
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