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Clustering macroeconomic variables

Chiara Perricone ()

Structural Change and Economic Dynamics, 2018, vol. 44, issue C, 23-33

Abstract: Numerous studies have highlighted the structural instability in certain macroeconomic time series. This issue has been typically addressed through three econometric methodologies: structural breaks, Regime-Switching, and time-varying parameter models, all requiring some ex ante structure to define the changes. Drawing on the recurrent Chinese restaurant process, a model for an autoregressive process is introduced and estimated via a particle filter. This methodology is employed to study the instability in post World War II US inflation. The application displays a good fit to the data, producing a clusterization of the time series that can be interpreted in terms of economic history, given a relative small number of estimated clusters. In addition, it is able to recover key data features without making restrictive assumptions, as in the case of one-break or time-varying parameter models.

Keywords: Evolutionary clustering; Non-parametric Bayesian analysis; Particle filter; Structural changes (search for similar items in EconPapers)
JEL-codes: C18 C22 C51 E17 (search for similar items in EconPapers)
Date: 2018
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Working Paper: Clustering Macroeconomic Variables (2013) Downloads
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