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A New Ridge Regression Causality Test in the Presence of Multicollinearity
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistics)
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistics)
Jönköping University, Jönköping International Business School, JIBS, Economics, Finance and Statistics. (Statistics)
2014 (English)In: Communications in Statistics - Theory and Methods, ISSN 0361-0926, E-ISSN 1532-415X, Vol. 43, no 2, 235-248 p.Article in journal (Refereed) Published
Abstract [en]

The VAR lag structure applied for the traditional Granger causality (GC) test is always severely affected by multicollinearity due to autocorrelation among the lags. Therefore, as a remedy to this problem we introduce a new Ridge Regression Granger Causality (RRGC) test, which is compared to the GC test by means of Monte Carlo simulations. Based on the simulation study we conclude that the traditional OLS version of the GC test over-rejects the true null hypothesis when there are relatively high (but empirically normal) levels of multicollinearity, while the new RRGC test will remedy or substantially decrease this problem.

Place, publisher, year, edition, pages
2014. Vol. 43, no 2, 235-248 p.
Keyword [en]
Granger causality test, Multicollinearity, Ridge parameters, Size and power
National Category
Social Sciences
Identifiers
URN: urn:nbn:se:hj:diva-17328DOI: 10.1080/03610926.2012.659825ISI: 000328930900002Scopus ID: 2-s2.0-84891588520OAI: oai:DiVA.org:hj-17328DiVA: diva2:480762
Available from: 2012-01-19 Created: 2012-01-19 Last updated: 2016-11-29Bibliographically approved

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Månsson, KristoferShukur, GhaziSjölander, Pär
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