Granger causality of bivariate stationary curve time series
| Autor: | Han Lin Shang, Ufuk Beyaztas, Kaiying Ji |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
050208 finance Index (economics) Series (mathematics) Strategy and Management Lag 05 social sciences Bivariate analysis Management Science and Operations Research Statistics - Applications Computer Science Applications Methodology (stat.ME) Causality (physics) Granger causality Modeling and Simulation 0502 economics and business Econometrics Applications (stat.AP) 97K80 94A16 050207 economics Statistics Probability and Uncertainty Predictability Lagging Statistics - Methodology Mathematics |
| Zdroj: | Journal of Forecasting. 40:626-635 |
| ISSN: | 1099-131X 0277-6693 |
| Popis: | 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 the 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. 17 pages, 3 figures, to appear at the Journal of Forecasting |
| Databáze: | OpenAIRE |
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