Factor double autoregressive models with application to simultaneous causality testing

Guo, Shaojun and Ling, Shiqing and Zhu, Ke (2013): Factor double autoregressive models with application to simultaneous causality testing.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Asymptotic Normality; Causality-in-mean; Causality-in-variance; Factor DAR model; Instantaneous causality; Score test; Strong consistency.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C12 - Hypothesis Testing: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling

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