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

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

Author

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  • Gary J. Cornwall
  • Jeffrey A. Mills
  • Beau A. Sauley
  • Huibin Weng

Abstract

This chapter develops a predictive approach to Granger causality (GC) testing that utilizesk-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-sampleF-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.

Suggested Citation

  • Gary J. Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2019. "Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation," Advances in Econometrics, in: Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, volume 40, pages 275-292, Emerald Group Publishing Limited.
    • Handle: RePEc:eme:aecozz:s0731-90532019000040a012
    DOI: 10.1108/S0731-90532019000040A012
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    More about this item

    Keywords

    Granger causality; predictive testing; posterior simulation; cross-validation; out-of-sample testing; Monte Carlo;
    All these keywords.

    JEL classification:

    • E60 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General

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