Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

Berkeley Electronic Press
Publication Type:
Journal Article
Studies in Nonlinear Dynamics and Econometrics, 2022, 26, (3), pp. 337-359
Issue Date:
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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.
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