Clustering Macroeconomic Time Series

Clustering Macroeconomic Time Series AbstractThe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Econometrics de Gruyter

Clustering Macroeconomic Time Series

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Publisher
de Gruyter
Copyright
© 2018 Iwo Augustyński et al., published by Sciendo
eISSN
2449-9994
D.O.I.
10.15611/eada.2018.2.06
Publisher site
See Article on Publisher Site

Abstract

AbstractThe 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.

Journal

Econometricsde Gruyter

Published: Jun 1, 2018

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