Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

Mengheng Li, Ivan Mendieta-Munoz

    Research output: Contribution to journalArticle

    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.
    Original languageEnglish
    JournalStudies in Nonlinear Dynamics and Econometrics
    Volume26
    Issue number3
    DOIs
    Publication statusPublished - 2021

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