Clustering Macroeconomic Time Series

Augustyński Iwo () and Laskoś-Grabowski Paweł ()
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Augustyński Iwo: Wrocław University of Economics, Wrocław, Poland
Laskoś-Grabowski Paweł: University of Wrocław, Institute of Theoretical Physics, Wrocław, Poland

Econometrics. Advances in Applied Data Analysis, 2018, vol. 22, issue 2, 74-88

Abstract: The data mining technique of time series clustering is well established. However, even when recognized as an unsupervised learning method, it does require making several design decisions that are nontrivially influenced by the nature of the data involved. By extensively testing various possibilities, we arrive at a choice of a dissimilarity measure (compression-based dissimilarity measure, or CDM) which is particularly suitable for clustering macroeconomic variables. We check that the results are stable in time and reflect large-scale phenomena, such as crises. We also successfully apply our findings to the analysis of national economies, specifically to identifying their structural relations.

Keywords: time series clustering; similarity; cluster analysis; GDP (search for similar items in EconPapers)
JEL-codes: C18 C63 E00 (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:eaiada:v:22:y:2018:i:2:p:74-88:n:6

DOI: 10.15611/eada.2018.2.06

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