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Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

  • Mengheng Li ORCID logo EMAIL logo and Ivan Mendieta-Muñoz ORCID logo


We propose a structural representation of the correlated unobserved components model, which allows for a structural interpretation of the interactions between trend and cycle shocks. We show that point identification of the full contemporaneous matrix which governs the structural interaction between trends and cycles can be achieved via heteroskedasticity. We develop an efficient Bayesian estimation procedure that breaks the multivariate problem into a recursion of univariate ones. An empirical implementation for the US Phillips curve shows that our model is able to identify the magnitude and direction of spillovers of the trend and cycle components both within-series and between-series.

JEL classification: C11; C32; E31; E32; E52

Corresponding author: Mengheng Li, Economics Discipline Group, University of Technology Sydney, UTS Business School, Ultimo, Sydney, NSW 2007, Australia; and Centre for Applied Macroeconomic Analysis, Australian National University, Canberra, Australia, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.


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Supplementary Material

The online version of this article offers supplementary material (

Received: 2020-02-27
Revised: 2021-05-02
Accepted: 2021-05-18
Published Online: 2021-06-03

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