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Clustering macroeconomic variables

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  • Perricone, Chiara

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.

Suggested Citation

  • Perricone, Chiara, 2018. "Clustering macroeconomic variables," Structural Change and Economic Dynamics, Elsevier, vol. 44(C), pages 23-33.
  • Handle: RePEc:eee:streco:v:44:y:2018:i:c:p:23-33
    DOI: 10.1016/j.strueco.2018.02.001
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    References listed on IDEAS

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    More about this item

    Keywords

    Evolutionary clustering; Non-parametric Bayesian analysis; Particle filter; Structural changes;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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