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Which Lag Length Selection Criteria Should We Employ?

Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting...

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Main Author: Khim−Sen Liew, Venus
Format: E-Article
Language:English
Published: Economics Bulletin 2004
Subjects:
AC Collections. Series. Collected works
HB Economic Theory
Online Access:http://ir.unimas.my/id/eprint/1865/1/Which%2BLag%2BLength%2BSelection%2BCriteria%2BShould%2BWe%2BEmploy%2B%2528abstract%2529%20%281%29%20%281%29.pdf
http://ir.unimas.my/id/eprint/1865/
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spelling my.unimas.ir.18652015-03-13T07:06:55Z http://ir.unimas.my/id/eprint/1865/ Which Lag Length Selection Criteria Should We Employ? Khim−Sen Liew, Venus AC Collections. Series. Collected works HB Economic Theory Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting finding of this study is that Akaike’s information criterion (AIC) and final prediction error (FPE) are superior than the other criteria under study in the case of small sample (60 observations and below), in the manners that they minimize the chance of under estimation while maximizing the chance of recovering the true lag length. One immediate econometric implication of this study is that as most economic sample data can seldom be considered “large” in size, AIC and FPE are recommended for the estimation the autoregressive lag length. Economics Bulletin 2004 E-Article NonPeerReviewed text en http://ir.unimas.my/id/eprint/1865/1/Which%2BLag%2BLength%2BSelection%2BCriteria%2BShould%2BWe%2BEmploy%2B%2528abstract%2529%20%281%29%20%281%29.pdf Khim−Sen Liew, Venus (2004) Which Lag Length Selection Criteria Should We Employ? Economics Bulletin, 3 (33). pp. 1-9.
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic AC Collections. Series. Collected works
HB Economic Theory
spellingShingle AC Collections. Series. Collected works
HB Economic Theory
Khim−Sen Liew, Venus
Which Lag Length Selection Criteria Should We Employ?
description Estimating the lag length of autoregressive process for a time series is a crucial econometric exercise in most economic studies. This study attempts to provide helpfully guidelines regarding the use of lag length selection criteria in determining the autoregressive lag length. The most interesting finding of this study is that Akaike’s information criterion (AIC) and final prediction error (FPE) are superior than the other criteria under study in the case of small sample (60 observations and below), in the manners that they minimize the chance of under estimation while maximizing the chance of recovering the true lag length. One immediate econometric implication of this study is that as most economic sample data can seldom be considered “large” in size, AIC and FPE are recommended for the estimation the autoregressive lag length.
format E-Article
author Khim−Sen Liew, Venus
author_facet Khim−Sen Liew, Venus
author_sort Khim−Sen Liew, Venus
title Which Lag Length Selection Criteria Should We Employ?
title_short Which Lag Length Selection Criteria Should We Employ?
title_full Which Lag Length Selection Criteria Should We Employ?
title_fullStr Which Lag Length Selection Criteria Should We Employ?
title_full_unstemmed Which Lag Length Selection Criteria Should We Employ?
title_sort which lag length selection criteria should we employ?
publisher Economics Bulletin
publishDate 2004
url http://ir.unimas.my/id/eprint/1865/1/Which%2BLag%2BLength%2BSelection%2BCriteria%2BShould%2BWe%2BEmploy%2B%2528abstract%2529%20%281%29%20%281%29.pdf
http://ir.unimas.my/id/eprint/1865/
_version_ 1644508954531725312
score 13.211869

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