Causality Distance Measures for Multivariate Time Series with Applications

Achilleas Anastasiou, Peter Hatzopoulos, Alex Karagrigoriou and George Mavridoglou
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Achilleas Anastasiou: Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, GR-83200 Samos, Greece
Peter Hatzopoulos: Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, GR-83200 Samos, Greece
Alex Karagrigoriou: Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, GR-83200 Samos, Greece
George Mavridoglou: Department of Accounting and Finance, University of Peloponnese, GR-24100 Antikalammos, Greece

Mathematics, 2021, vol. 9, issue 21, 1-15

Abstract: In this work, we focus on the development of new distance measure algorithms, namely, the Causality Within Groups (CAWG), the Generalized Causality Within Groups (GCAWG) and the Causality Between Groups (CABG), all of which are based on the well-known Granger causality. The proposed distances together with the associated algorithms are suitable for multivariate statistical data analysis including unsupervised classification (clustering) purposes for the analysis of multivariate time series data with emphasis on financial and economic data where causal relationships are frequently present. For exploring the appropriateness of the proposed methodology, we implement, for illustrative purposes, the proposed algorithms to hierarchical clustering for the classification of 19 EU countries based on seven variables related to health resources in healthcare systems.

Keywords: multivariate time series; Granger causality; clustering; classification; distance; divergence; healthcare systems; pattern recognition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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