Clustering Macroeconomic Variables

Chiara Perricone ()

No 283, CEIS Research Paper from Tor Vergata University, CEIS

Abstract: Many papers have highlighted that some macroeconomic time series present structural instability. The causes of these remarkable changes in the reduced form properties of the macroeconomy is a debated argument. In literature this issue is handled with three main econometric methodologies: structural breaks, regime-switching and time-varying parameters (TVP). Nevertheless all these approaches need some ex ante structure in order to model the change. Based on the Recurrent Chinese Restaurant Process, I have specified a model for an autoregressive process and estimated via particle filter using a conjugate prior, which applied the idea of evolutionary cluster to the study of the instability in output and inflation for US after War World II. This procedure displays some advantages, in particular does not require a strong ex ante structure in order to neither detect the breaks nor manage the evolution of parameters. The application of the cluster procedure to GDP growth and inflation rate for US from 1957 to 2011 shows a good ability in fit the data, moreover it produces a clusterization of the time series that could be interpreted in terms of economic history and it is able to recover key data features without making restrictive assumptions, as in âone-breakâ or TVP models.

JEL-codes: C18 C22 C51 E17 (search for similar items in EconPapers)
Pages: 27 pages
Date: 2013-06-11, Revised 2013-06-11
New Economics Papers: this item is included in nep-ecm and nep-his
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Journal Article: Clustering macroeconomic variables (2018)
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