DP9655 Testing for Granger Causality with Mixed Frequency Data
|Author(s):||Eric Ghysels, Jonathan B. Hill, Kaiji Motegi|
|Publication Date:||September 2013|
|Keyword(s):||Granger causality, mixed data sampling (MIDAS), temporal aggression, vector autoregression (VAR)|
|Programme Areas:||Financial Economics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=9655|
It is well known that temporal aggregation has adverse effects on Granger causality tests. Time series are often sampled at different frequencies. This is typically ignored, and data are merely aggregated to the common lowest frequency. We develop a set of Granger causality tests that explicitly take advantage of data sampled at different frequencies. We show that taking advantage of mixed frequency data allows us to better recover causal relationships when compared to the conventional common low frequency approach. We also show that the mixed frequency causality tests have higher local asymptotic power as well as more power in finite samples compared to conventional tests.