TY  - CHAP
AB  - 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-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.
VL  - 40A
SN  - 978-1-78973-241-2, 978-1-78973-242-9/0731-9053
DO  - 10.1108/S0731-90532019000040A012
UR  - https://doi.org/10.1108/S0731-90532019000040A012
AU  - Cornwall Gary J.
AU  - Mills Jeffrey A.
AU  - Sauley Beau A.
AU  - Weng Huibin
PY  - 2019
Y1  - 2019/01/01
TI  - Predictive Testing for Granger Causality via Posterior Simulation and Cross-validation
T2  - Topics in Identification, Limited Dependent Variables, Partial Observability, Experimentation, and Flexible Modeling: Part A
T3  - Advances in Econometrics
PB  - Emerald Publishing Limited
SP  - 275
EP  - 292
Y2  - 2024/04/26
ER  -