Title

A new nonlinearity test to circumvent the limitation of Volterra expansion with application

Document Type

Journal article

Source Publication

Journal of the Korean Statistical Society

Publication Date

9-1-2017

Volume

46

Issue

3

First Page

365

Last Page

374

Publisher

Korean Statistical Society

Keywords

Dependence, Dependent test, Nonlinear test, Nonlinearity, Sunspots, Volterra expansion

Abstract

In this paper, we propose a quick and efficient method to examine whether a time series Xt possesses any nonlinear feature by testing a kind of dependence remained in the residuals after fitting Xt with a linear model. 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 the variable being examined. Another advantage of our proposed test is that there is no over-rejection problem which exists in some famous nonlinearity tests. Our proposed test 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. The conclusion drawn from our proposed test is consistent with those from other well-established tests.

DOI

10.1016/j.jkss.2016.11.006

Print ISSN

12263192

E-ISSN

18764231

Publisher Statement

Copyright © 2016 The Korean Statistical Society. Access to external full text or publisher's version may require subscription.

Full-text Version

Publisher’s Version

Recommended Citation

Hui, Y., Wong, W.-K., Bai, Z., & Zhu, Z.-Z. (2017). A new nonlinearity test to circumvent the limitation of Volterra expansion with application. Journal of the Korean Statistical Society, 46(3), 365-374. doi: 10.1016/j.jkss.2016.11.006

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