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A Test of Sufficient Condition for Infinite-step Granger Noncausality in Infinite Order Vector Autoregressive Process

Umberto Triacca (), Olivier Damette and Alessandro Giovannelli ()
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Umberto Triacca: University of L'Aquila

No 496, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: This paper derives a sufficient condition for noncausality at all forecast horizons (infinitestep noncausality). We propose a test procedure for this sufficient condition. Our procedure presents two main advantages. First, our infinite-step Granger causality analysis is conducted in a more general framework with respect to the procedures proposed in literature. Second, it involves only linear restrictions under the null, that can be tested by using standard F statistics. A simulation study shows that the proposed procedure has reasonable size and good power. Typically, one thousand or more observations are required to ensure that the test procedures perform reasonably well. These are typical sample sizes for financial time series applications. Here, we give a first example of possible applications by considering the Mixture Distribution Hypothesis in the Foreign Exchange Market

Keywords: Granger causality; Hypothesis testing; Time series; Vector autoregressive Models (search for similar items in EconPapers)
JEL-codes: C12 C22 C58 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2020-06-18, Revised 2020-06-18
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