Economics at your fingertips  

Clustering Macroeconomic Time Series

Iwo Augustyński and Laskoś-Grabowski, Paweł

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

Abstract: 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.

Keywords: time series clustering; similarity; cluster analysis; GDP (search for similar items in EconPapers)
JEL-codes: E00 C18 C63 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-mac
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in EconStor Preprints from ZBW - Leibniz Information Centre for Economics Contact information at EDIRC.
Bibliographic data for series maintained by ZBW - Leibniz Information Centre for Economics ().

Page updated 2020-01-18
Handle: RePEc:zbw:esprep:171380