Testing for Granger causality with mixed frequency data

Eric Ghysels, Jonathan B. Hill and Kaiji Motegi

Journal of Econometrics, 2016, vol. 192, issue 1, 207-230

Abstract: We develop Granger causality tests that apply directly to 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 new causality tests have higher local asymptotic power as well as more power in finite samples compared to conventional tests. In an empirical application involving U.S. macroeconomic indicators, we show that the mixed frequency approach and the low frequency approach produce very different causal implications, with the former yielding more intuitively appealing result.

Keywords: Granger causality test; Local asymptotic power; Mixed data sampling (MIDAS); Temporal aggregation; Vector autoregression (VAR) (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (53)

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Related works:
Working Paper: Testing for Granger Causality with Mixed Frequency Data (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:192:y:2016:i:1:p:207-230

DOI: 10.1016/j.jeconom.2015.07.007

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