Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation

Gary Cornwall, Jeffrey Mills, Beau Sauley and Huibin Weng
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Huibin Weng: Bureau of Economic Analysis

BEA Working Papers from 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.

JEL-codes: E60 (search for similar items in EconPapers)
Date: 2018-09
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https://www.bea.gov/system/files/papers/WP2018-10_1.pdf (application/pdf)

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Chapter: Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:bea:wpaper:0156

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