A New Nonlinearity Test to Circumvent the Limitation of Volterra Expansion with Applications

Yongchang Hui, Wing-Keung Wong, Zhidong Bai and Zhu Zhenzhen ()

MPRA Paper from University Library of Munich, Germany

Abstract: In this paper, we propose a quick, efficient, and easy method to examine whether a time series Yt possesses any nonlinear feature. The advantage of our proposed nonlinearity test is that it is not required to know the exact nonlinear features and the detailed nonlinear forms of Yt. We find that our proposed test can be used to detect any nonlinearity for the variable being examined and detect GARCH models in the innovations. It can also be used to test whether the hypothesized model, including linear and nonlinear, to the variable being examined is appropriate as long as the residuals of the model being used can be estimated. Our simulation study shows that our proposed test is stable and powerful. We apply our proposed statistic to test whether there is any nonlinear feature in the sunspot data and whether the S&P 500 index follows a random walk model. The conclusion drawn from our proposed test is consistent those from other tests.

Keywords: Nonlinearity; U-statistics; Volterra expansion; sunspots; efficient market (search for similar items in EconPapers)
JEL-codes: C01 C12 G10 (search for similar items in EconPapers)
Date: 2016-11-22
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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Related works:
Working Paper: A New Nonlinearity Test to Circumvent the Limitation of Volterra Expansion with Application (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:75216

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