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Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

Li Mengheng () and Ivan Mendieta-Muñoz
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Li Mengheng: Economics Discipline Group, University of Technology Sydney, Sydney, Ultimo, Australia

Studies in Nonlinear Dynamics & Econometrics, 2022, vol. 26, issue 3, 337-359

Abstract: 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.

Keywords: identification via heteroskedasticity; permanent and transitory shocks; spillover structural effects; state space models; trends and cycles; unobserved components; C11; C32; E31; E32; E52 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1515/snde-2020-0027

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