Bayesian Testing Of Granger Causality In Functional Time Series

Fuente: arXiv
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Autores principales: Sen, Rituparna, Majumdar, Anandamayee, Sikaria, Shubhangi
Formato: Preprint
Publicado: 2021
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author Sen, Rituparna
Majumdar, Anandamayee
Sikaria, Shubhangi
author_facet Sen, Rituparna
Majumdar, Anandamayee
Sikaria, Shubhangi
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2112_15315
institution arXiv
publishDate 2021
record_format arxiv
spellingShingle Bayesian Testing Of Granger Causality In Functional Time Series
Sen, Rituparna
Majumdar, Anandamayee
Sikaria, Shubhangi
Methodology
Statistical Finance
Applications
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.
title Bayesian Testing Of Granger Causality In Functional Time Series
topic Methodology
Statistical Finance
Applications
url https://arxiv.org/abs/2112.15315