Bayesian Testing Of Granger Causality In Functional Time Series

TL;DR

This paper introduces a Bayesian approach to test Granger causality in multivariate functional time series using a novel MFAR model and Bayes Factor, with applications in finance and climatology.

Contribution

It develops a Bayesian multivariate functional autoregressive model and a method to test Granger causality between functional time series.

Findings

Demonstrates causality detection between yield curves of different countries.

Shows weather conditions can Granger cause pollutant levels.

Improves forecast accuracy through the MFAR model.

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.