Testing Non-linearity Using a Modified Q Test

Marian Vavra ()

No 1204, Birkbeck Working Papers in Economics and Finance from Birkbeck, Department of Economics, Mathematics & Statistics

Abstract: A new version of the Q test, based on generalized residual correlations (i.e. auto-correlations and cross-correlations), is developed in this paper. The Q test fixes two main shortcomings of the Mcleod and Li Q (MLQ) test often used in the literature: (i) the test is capable to capture some interesting non-linear models, for which the original MLQ test completely fails (e.g. a non-linear moving average model). Additionally, the Q test also significantly improves the power for some other non-linear models (e.g. a threshold moving average model), for which the original MLQ test does not work very well; (ii) the new Q test can be used for discrimination between simple and more complicated (non-linear/asymmetric) GARCH models as well.

Keywords: non-linearity testing; portmanteau Q test; auto-correlation; cross-correlation (search for similar items in EconPapers)
JEL-codes: C12 C15 C32 C46 (search for similar items in EconPapers)
Date: 2012-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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https://eprints.bbk.ac.uk/id/eprint/5955 First version, 2012 (application/pdf)

Related works:
Journal Article: Portmanteau tests for linearity of stationary time series (2019)
Working Paper: Portmanteau Tests for Linearity of Stationary Time Series (2016)
Working Paper: Portmanteau Tests for Linearity of Stationary Time Series (2015)
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