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Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation

aBureau of Economic Analysis, United States
bUniversity of Cincinnati, United States

Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A

ISBN: 978-1-78973-242-9, eISBN: 978-1-78973-241-2

ISSN: 0731-9053

Publication date: 30 August 2019

Abstract

This chapter develops a predictive approach to Granger causality (GC) testing that utilizes k -fold cross-validation and posterior simulation to perform out-of-sample testing. A Monte Carlo study indicates that the cross-validation predictive procedure has improved power in comparison to previously available out-of-sample testing procedures, matching the performance of the in-sample F-test while retaining the credibility of post- sample inference. An empirical application to the Phillips curve is provided evaluating the evidence on GC between inflation and unemployment rates.

Keywords

Acknowledgements

Acknowledgments

The authors gratefully acknowledge support from the Taft Fund, University of Cincinnati, and feedback from Rick Ashley and participants at the Midwest Econometrics Group Meetings. Any views expressed here are those of the authors and not necessarily those of the Bureau of Economic Analysis or US Department of Commerce.

Citation

Cornwall, G.J., Mills, J.A., Sauley, B.A. and Weng, H. (2019), "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation", Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A (Advances in Econometrics, Vol. 40A), Emerald Publishing Limited, Bingley, pp. 275-292. https://doi.org/10.1108/S0731-90532019000040A012

Publisher

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Emerald Publishing Limited

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