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

Gary Cornwall, Jeffrey A. Mills, Beau A. Sauley and Huibin Weng

A chapter in Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A, 2019, vol. 40A, pp 275-292 from Emerald Group Publishing Limited

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.

Keywords: Granger causality; predictive testing; posterior simulation; cross-validation; out-of-sample testing; Monte Carlo (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... 1-90532019000040A012
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)

Related works:
Working Paper: Predictive Testing for Granger Causality via Posterior Simulation and Cross Validation (2018)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:aecozz:s0731-90532019000040a012

DOI: 10.1108/S0731-90532019000040A012

Access Statistics for this chapter

More chapters in Advances in Econometrics from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().