 Clustering macroeconomic variablesStructural 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) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:streco:v:44:y:2018:i:c:p:23-33 DOI: 10.1016/j.strueco.2018.02.001 Access Statistics for this article Structural Change and Economic Dynamics is currently edited by F. Duchin, H. Hagemann, M. Landesmann, R. Scazzieri, A. Steenge and B. Verspagen More articles in Structural Change and Economic Dynamics from Elsevier |
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