Clustering Macroeconomic Time Series

Preprint

There is growing literature in macroeconomics, especially on business cycle synchronization, employing different methods of time series clustering. However, even as an unsupervised learning method, this technique requires making choices 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 consistent with the literature on core-periphery pattern of European business cycles. We also successfully apply our findings to the analysis of national economies, specifically to identifying their structural relations. To our knowledge, it is the first comprehensive analysis of the usefulness of the different dissimilarity measures for the macroeconomic research.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: http://hdl.handle.net/10419/180670

Classification and Topics

• Classification: Wirtschaft ; Macroeconomics and Monetary Economics: General ; Methodological Issues: General ; Computational Techniques; Simulation Modeling
• Subject: time series clustering ; similarity ; cluster analysis ; GDP

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Augustyński, Iwo ; Laskoś-Grabowski, Paweł
• Event: Veröffentlichung
• (who): ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften, Leibniz-Informationszentrum Wirtschaft
• (where): Kiel und Hamburg
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/171380
• Last update: 10.03.2025, 11:44 AM CET