Bayesian Testing Of Granger Causality In Functional Time Series

Rituparna Sen, Anandamayee Majumdar and Shubhangi Sikaria

Papers from arXiv.org

Abstract: We develop a multivariate functional autoregressive model (MFAR), which captures the cross-correlation among multiple functional time series and thus improves forecast accuracy. We estimate the parameters under the Bayesian dynamic linear models (DLM) framework. In order to capture Granger causality from one FAR series to another we employ Bayes Factor. Motivated by the broad application of functional data in finance, we investigate the causality between the yield curves of two countries. Furthermore, we illustrate a climatology example, examining whether the weather conditions Granger cause pollutant daily levels in a city.

Date: 2021-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-his
References: View references in EconPapers View complete reference list from CitEc
Citations:

Published in 2021 Journal of Statistical Theory and Practice, 15: 40

Downloads: (external link)
http://arxiv.org/pdf/2112.15315 Latest version (application/pdf)

Related works:
Journal Article: Bayesian Testing of Granger Causality in Functional Time Series (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2112.15315

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().