On testing for nonlinearity in multivariate time series

This paper considers a multivariate extension of the test for neglected nonlinearity proposed by Tsay (1986) that uses principal components to overcome the problem of dimensionality that is common with tests of this type. Monte Carlo experiments reveal that the modified multivariate test provides a significant dimensional reduction without suffering from any systematic level distortion or power loss, and is more powerful than univariate nonlinearity tests.

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Year of publication:	2014
Authors:	Psaradakis, Zacharias ; Vávra, Marián
Published in:	Economics Letters. - Elsevier, ISSN 0165-1765. - Vol. 125.2014, 1, p. 1-4
Publisher:	Elsevier
Subject:	Multivariate time series \| Nonlinearity tests \| Principal components

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Type of publication:	Article
Classification:	C12 - Hypothesis Testing ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C32 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011041605