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Article: Factor double autoregressive models with application to simultaneous causality testing

TitleFactor double autoregressive models with application to simultaneous causality testing
Authors
KeywordsCausality-in-variance
Score test
Instantaneous causality
Strong consistency
Asymptotic normality
Causality-in-mean
Factor DAR model
Issue Date2014
Citation
Journal of Statistical Planning and Inference, 2014, v. 148, p. 82-94 How to Cite?
AbstractTesting causality-in-mean and causality-in-variance has been largely studied. However, none of the tests can detect causality-in-mean and causality-in-variance simultaneously. In this paper, we introduce a factor double autoregressive (FDAR) model. Based on this model, a score test is proposed to detect causality-in-mean and causality-in-variance simultaneously. Furthermore, strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for the FDAR model are established. A small simulation study shows good performances of the QMLE and the score test in finite samples. A real data example on the causal relationship between Hong Kong stock market and US stock market is given. © 2013 Elsevier B.V.
Persistent Identifierhttp://hdl.handle.net/10722/230952
ISSN
2015 Impact Factor: 0.727
2015 SCImago Journal Rankings: 1.090

 

DC FieldValueLanguage
dc.contributor.authorGuo, Shaojun-
dc.contributor.authorLing, Shiqing-
dc.contributor.authorZhu, Ke-
dc.date.accessioned2016-09-01T06:07:14Z-
dc.date.available2016-09-01T06:07:14Z-
dc.date.issued2014-
dc.identifier.citationJournal of Statistical Planning and Inference, 2014, v. 148, p. 82-94-
dc.identifier.issn0378-3758-
dc.identifier.urihttp://hdl.handle.net/10722/230952-
dc.description.abstractTesting causality-in-mean and causality-in-variance has been largely studied. However, none of the tests can detect causality-in-mean and causality-in-variance simultaneously. In this paper, we introduce a factor double autoregressive (FDAR) model. Based on this model, a score test is proposed to detect causality-in-mean and causality-in-variance simultaneously. Furthermore, strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for the FDAR model are established. A small simulation study shows good performances of the QMLE and the score test in finite samples. A real data example on the causal relationship between Hong Kong stock market and US stock market is given. © 2013 Elsevier B.V.-
dc.languageeng-
dc.relation.ispartofJournal of Statistical Planning and Inference-
dc.subjectCausality-in-variance-
dc.subjectScore test-
dc.subjectInstantaneous causality-
dc.subjectStrong consistency-
dc.subjectAsymptotic normality-
dc.subjectCausality-in-mean-
dc.subjectFactor DAR model-
dc.titleFactor double autoregressive models with application to simultaneous causality testing-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.jspi.2013.12.007-
dc.identifier.scopuseid_2-s2.0-84897640281-
dc.identifier.volume148-
dc.identifier.spage82-
dc.identifier.epage94-

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