Testing for Granger causality with mixed frequency data

Eric Ghysels, Jonathan B. Hill, Kaiji Motegi

    研究成果: Article

    23 引用 (Scopus)

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    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.

    元の言語English
    ページ(範囲)207-230
    ページ数24
    ジャーナルJournal of Econometrics
    192
    発行部数1
    DOI
    出版物ステータスPublished - 2016 5 1

    ASJC Scopus subject areas

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

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