الگوی مرکز- پیرامون چرخه تجاری اروپا: یک رهیافت خوشه ای فازی The core–periphery pattern of European business cycles: A fuzzy clustering approach

1. Introduction Since the adoption of a single European currency in the early 1990s, the synchronization of business cycles between European economies has become a major field of both theoretical and empirical research. The main objective of this literature is to investigate the extent to which a common ‘European business cycle’ is established that applies as a basic condition for a smoothly working monetary union (Artis et al. 2004). In fact, the global financial crisis and the subsequent euro crisis have rather provided evidence of large economic discrepancies primarily between groups of countries within and beyond the euro area (EA). Therefore, cyclical (dis)similarities should be considered from a group perspective, for instance between the ‘vulnerable’ economies in Southern Europe (European Commission 2014) or the Central and Eastern European countries (CEECs; Fidrmuc and Korhonen 2006; Stanisic 2013; Di Giorgio 2016) and the Central European countries. A conventional scheme for the analysis of business cycle patterns among groups of (prospective) EA members is the core‒periphery division (Camacho et al. 2006). As opposed to the Southern, the Eastern and sometimes the Northern European ‘periphery,’ a homogeneous ‘core’ group is typically identified among the founding EU Member States, with Germany at its center (see, for instance, Arestis and Phelps 2016). Assuming that the supposed core countries share similar business cycles, say the ‘European core business cycle,’ policy makers may thus be interested in how closely countries are associated with this cycle compared with other group-specific European cycles. However, the identification of core and peripheral European business cycles and the potential group composition remain inconsistent in the literature. In this paper we propose a more comprehensive way to explore the core‒periphery pattern empirically by conducting a fuzzy cluster analysis of business cycle time series, which allows us to provide detailed information on countries’ accordance with group-specific European business cycles.