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.

Language: Englisch

Bibliographic citation: Journal: Econometrics ; ISSN: 2225-1146 ; Volume: 5 ; Year: 2017 ; Issue: 1 ; Pages: 1-16 ; Basel: MDPI

Classification: Wirtschaft
Econometrics
Large Data Sets: Modeling and Analysis
Financial Econometrics
Computational Techniques; Simulation Modeling
Financial Forecasting and Simulation

Subject: GMM
HAC estimation
Newey-West estimator
Toeplitz matrices
discrete Fourier transformation (DFT)

Event: Geistige Schöpfung

(who): Heberle, Jochen
Sattarhoff, Cristina

Event: Veröffentlichung

(who): MDPI

(where): Basel

(when): 2017

DOI: doi:10.3390/econometrics5010009

Handle: http://hdl.handle.net/10419/171904

Last update: 10.03.2025, 11:45 AM CET

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Artikel

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Heberle, Jochen
Sattarhoff, Cristina
MDPI

Time of origin

2017

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A fast algorithm for the computation of HAC covariance matrix estimators

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