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Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation

Author

Listed:
  • Gary Cornwall
  • Jeffrey A. Mills
  • Beau A. Sauley
  • Huibin Weng

    (Bureau of Economic Analysis)

Abstract

This paper develops a predictive approach to Granger causality 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 Granger causality between in ation and unemployment rates.

Suggested Citation

  • Gary Cornwall & Jeffrey A. Mills & Beau A. Sauley & Huibin Weng, 2018. "Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation," BEA Working Papers 0156, Bureau of Economic Analysis.
    • Handle: RePEc:bea:wpaper:0156
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    References listed on IDEAS

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    JEL classification:

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

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