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Journal of Time Series Econometrics

Editor-in-Chief: Hidalgo, Javier

2 Issues per year


CiteScore 2017: 0.25

SCImago Journal Rank (SJR) 2017: 0.236
Source Normalized Impact per Paper (SNIP) 2017: 0.682

Mathematical Citation Quotient (MCQ) 2016: 0.10

Online
ISSN
1941-1928
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Bootstrap, Jackknife and COLS: Bias and Mean Squared Error in Estimation of Autoregressive Models

Gareth D. Liu-Evans / Garry D. A. Phillips
Published Online: 2012-11-14 | DOI: https://doi.org/10.1515/1941-1928.1122

Abstract

We compare a number of bias-correction methodologies in terms of mean squared error and remaining bias, including the residual bootstrap, the relatively unexplored Quenouille jackknife, and methods based on analytical approximation of moments. We introduce a new higher-order jackknife estimator for the AR(1) with constant. Simulation results are presented for four different error structures, including GARCH. We include results for a relatively extreme situation where the errors are highly skewed and leptokurtic. It is argued that the bootstrap and analytical-correction (COLS) approaches are to be favoured overall, though the jackknife methods are the least biased. We find that COLS tends to have the lowest mean squared error, though the bootstrap also does well.

Keywords: autoregression; bias; efficiency; mean squared error; bootstrap; jackknife; moment approximation; non-normal

About the article

Published Online: 2012-11-14


Citation Information: Journal of Time Series Econometrics, Volume 4, Issue 2, ISSN (Online) 1941-1928, DOI: https://doi.org/10.1515/1941-1928.1122.

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©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston.Get Permission

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