GOODNESS-OF-FIT TEST FOR NONLINEAR TIME SERIES MODELS

Ngai Sze Han and Shiqing Ling
Additional contact information
Ngai Sze Han: Hong Kong Examinations and Assessment Authority, Hong Kong
Shiqing Ling: #x2020;Hong Kong University of Science and Technology, Department of Mathematics, Clear Water Bay, Hong Kong

Annals of Financial Economics (AFE), 2017, vol. 12, issue 02, 1-21

Abstract: Many time series models have been used extensively in modeling economic and financial data. However, it is difficult to determine the functional forms of the conditional mean and conditional variance in these models. In this paper, a test statistic based on the squared conditional residuals is proposed for testing these functional forms, and the asymptotic distribution of the test statistic is obtained. The test statistic is applicable not only to the family of GARCH models but also to other nonlinear time series models. Simulation results show that the proposed tests are powerful and have reasonable sizes. Two real examples are also given to illustrate our theory.

Keywords: ARMA model; GARCH model; goodness-of-fit test; test statistic and time series models (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S2010495217500063
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:afexxx:v:12:y:2017:i:02:n:s2010495217500063

Ordering information: This journal article can be ordered from

DOI: 10.1142/S2010495217500063

Access Statistics for this article

Annals of Financial Economics (AFE) is currently edited by Michael McAleer

More articles in Annals of Financial Economics (AFE) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().