Clustering Macroeconomic Time Series

TL;DR

This paper evaluates and refines time series clustering methods for macroeconomic data, identifying a compression-based dissimilarity measure as particularly effective for analyzing economic variables and their structural relations.

Contribution

It develops a tailored clustering methodology for macroeconomic time series, validating its stability and effectiveness in capturing large-scale economic phenomena.

Findings

CDM is suitable for macroeconomic clustering

Results are stable over time

Method reveals structural economic relations

Abstract

The data mining technique of time series clustering is well established in many fields. However, as an unsupervised learning method, it requires making choices that are nontrivially influenced by the nature of the data involved. The aim of this paper is to verify usefulness of the time series clustering method for macroeconomics research, and to develop the most suitable methodology. 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 analysis of national economies, specifically to identifying their structural relations.