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OUT-OF-SAMPLE TESTS FOR GRANGER CAUSALITY

Published online by Cambridge University Press:  21 November 2002

John Chao
Affiliation:
University of Maryland
Valentina Corradi
Affiliation:
University of Exeter
Norman R. Swanson
Affiliation:
Purdue University

Abstract

Clive W.J. Granger has summarized his personal viewpoint on testing for causality in numerous articles over the past 30 years and has outlined what he considers to be a useful operational version of his original definition of Granger causality, which he notes is partially alluded to in the Ph.D. dissertation of Norbert Wiener. This operational version of Granger causality is based on a comparison of the one-step-ahead predictive ability of competing models. However, Granger concludes his discussion by noting that it is common practice to test for Granger causality using in-sample F-tests. The practice of using in-sample type Granger causality tests continues to be prevalent. In this paper we develop simple (nonlinear) out-of-sample predictive ability tests of the Granger non-causality null hypothesis. In addition, Monte Carlo experiments are used to investigate the finite sample properites of the test. An empirical illustration shows that the choice of in-sample versus out-of-sample Granger causality tests can crucially affect the conclusions about the predictive content of money for output.

Type
Research Article
Copyright
© 2001 Cambridge University Press

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