Factor double autoregressive models with application to simultaneous causality testing

Shaojun Guo, Shiqing Ling () and Ke Zhu ()

MPRA Paper from University Library of Munich, Germany

Abstract: Testing 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 article, 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.

Keywords: Asymptotic Normality; Causality-in-mean; Causality-in-variance; Factor DAR model; Instantaneous causality; Score test; Strong consistency. (search for similar items in EconPapers)
JEL-codes: C1 C12 C5 (search for similar items in EconPapers)
Date: 2013-11-09
New Economics Papers: this item is included in nep-ecm and nep-ets
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