A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in finite samples. The empirical relevance of the test is illustrated via an application. © 2013 Elsevier B.V.

Testing for Granger non-causality using the autoregressive metric / DI IORIO, Francesca; Triacca, U.. - In: ECONOMIC MODELLING. - ISSN 1873-6122. - 33:(2013), pp. 120-125. [10.1016/j.econmod.2013.03.023]

Testing for Granger non-causality using the autoregressive metric

DI IORIO, FRANCESCA;
2013

Abstract

A new non-causality test based on the notion of distance between ARMA models is proposed in this paper. The advantage of this test is that it can be used in possible integrated and cointegrated systems, without pre-testing for unit roots and cointegration. The Monte Carlo experiments indicate that the proposed method performs reasonably well in finite samples. The empirical relevance of the test is illustrated via an application. © 2013 Elsevier B.V.
2013
Testing for Granger non-causality using the autoregressive metric / DI IORIO, Francesca; Triacca, U.. - In: ECONOMIC MODELLING. - ISSN 1873-6122. - 33:(2013), pp. 120-125. [10.1016/j.econmod.2013.03.023]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/542709
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