Testing for Granger causality with mixed frequency data

Eric Ghysels*, Jonathan B. Hill, Kaiji Motegi

*この研究の対応する著者

    研究成果: Article査読

    35 被引用数 (Scopus)

    抄録

    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.

    本文言語English
    ページ(範囲)207-230
    ページ数24
    ジャーナルJournal of Econometrics
    192
    1
    DOI
    出版ステータスPublished - 2016 5 1

    ASJC Scopus subject areas

    • 経済学、計量経済学
    • 応用数学
    • 科学史および科学哲学

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