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Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

Bayesian analysis of structural correlated unobserved components and identification via... AbstractWe 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Studies in Nonlinear Dynamics & Econometrics de Gruyter

Bayesian analysis of structural correlated unobserved components and identification via heteroskedasticity

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Publisher
de Gruyter
Copyright
© 2021 Walter de Gruyter GmbH, Berlin/Boston
ISSN
1558-3708
eISSN
1558-3708
DOI
10.1515/snde-2020-0027
Publisher site
See Article on Publisher Site

Abstract

AbstractWe 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.

Journal

Studies in Nonlinear Dynamics & Econometricsde Gruyter

Published: Jun 1, 2022

Keywords: identification via heteroskedasticity; permanent and transitory shocks; spillover structural effects; state space models; trends and cycles; unobserved components; C11; C32; E31; E32; E52

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