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

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Abstract
Testing causalityinmean and causalityinvariance has been largely studied. However, none of the tests can detect causalityinmean and causalityinvariance simultaneously. In this article, we introduce a factor double autoregressive (FDAR) model. Based on this model, a score test is proposed to detect causalityinmean and causalityinvariance simultaneously. Furthermore, strong consistency and asymptotic normality of the quasimaximum 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 

Original Title:  Factor double autoregressive models with application to simultaneous causality testing 
English Title:  Factor double autoregressive models with application to simultaneous causality testing 
Language:  English 
Keywords:  Asymptotic Normality; Causalityinmean; Causalityinvariance; 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 
Item ID:  51570 
Depositing User:  Dr. Ke Zhu 
Date Deposited:  19 Nov 2013 14:54 
Last Modified:  28 Sep 2019 13:38 
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URI:  https://mpra.ub.unimuenchen.de/id/eprint/51570 