{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-bootstrap-test-to-detect-prominent-granger","title":"A bootstrap test to detect prominent Granger-causalities across frequencies","arxiv_id":"1803.00374","date":"2018-03-01","proceeding":null,"authors":["Matteo Farné","Angela Montanari"],"abstract":"Granger-causality in the frequency domain is an emerging tool to analyze the\ncausal relationship between two time series. We propose a bootstrap test on\nunconditional and conditional Granger-causality spectra, as well as on their\ndifference, to catch particularly prominent causality cycles in relative terms.\nIn particular, we consider a stochastic process derived applying independently\nthe stationary bootstrap to the original series. Our null hypothesis is that\neach causality or causality difference is equal to the median across\nfrequencies computed on that process. In this way, we are able to disambiguate\ncausalities which depart significantly from the median one obtained ignoring\nthe causality structure. Our test shows power one as the process tends to\nnon-stationarity, thus being more conservative than parametric alternatives. As\nan example, we infer about the relationship between money stock and GDP in the\nEuro Area via our approach, considering inflation, unemployment and interest\nrates as conditioning variables. We point out that during the period 1999-2017\nthe money stock aggregate M1 had a significant impact on economic output at all\nfrequencies, while the opposite relationship is significant only at high\nfrequencies.","url_abs":"http://arxiv.org/abs/1803.00374v2","url_pdf":"http://arxiv.org/pdf/1803.00374v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-bootstrap-test-to-detect-prominent-granger","repo_url":"https://github.com/MatFar88/grangers","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}