Granger causality of bivariate stationary curve time series

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Shang H. L., Ji K., Beyaztas U.

JOURNAL OF FORECASTING, cilt.40, sa.4, ss.626-635, 2021 (SSCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 40 Sayı: 4
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1002/for.2732
  • Dergi Adı: JOURNAL OF FORECASTING
  • Derginin Tarandığı İndeksler: Social Sciences Citation Index (SSCI), Scopus, International Bibliography of Social Sciences, ABI/INFORM, Business Source Elite, Business Source Premier, Compendex, EconLit, INSPEC, Public Affairs Index, Sociological abstracts, zbMATH
  • Sayfa Sayıları: ss.626-635
  • Anahtar Kelimeler: functional time series, G&#8208, causality, Granger causality, GENERALIZED CORRELATION, INFERENCE
  • Marmara Üniversitesi Adresli: Evet

Özet

We study causality between bivariate curve time series using the Granger causality generalized measures of correlation. With this measure, we can investigate which curve time series Granger-causes the other; in turn, it helps determine the predictability of any two curve time series. Illustrated by a climatology example, we find that the sea surface temperature Granger-causes sea-level atmospheric pressure. Motivated by a portfolio management application in finance, we single out those stocks that lead or lag behind Dow Jones industrial averages. Given a close relationship between S&P 500 index and crude oil price, we determine the leading and lagging variables.