Testing for Granger causality with mixed frequency data

Eric Ghysels*, Jonathan B. Hill, Kaiji Motegi

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    63 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)207-230
    Number of pages24
    JournalJournal of Econometrics
    Issue number1
    Publication statusPublished - 2016 May 1


    • Granger causality test
    • Local asymptotic power
    • Mixed data sampling (MIDAS)
    • Temporal aggregation
    • Vector autoregression (VAR)

    ASJC Scopus subject areas

    • Economics and Econometrics
    • Applied Mathematics
    • History and Philosophy of Science


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