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The marginal likelihood of Structural Time Series Models, with application to the euro area and US NAIRU

Christophe Planas (), Alessandro Rossi () and Gabriele Fiorentini ()
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Christophe Planas: Joint Research Centre of the European Commission

Working Paper series from Rimini Centre for Economic Analysis

Abstract: We propose a simple procedure for evaluating the marginal likelihood in univariate Structural Time Series (STS) models. For this we exploit the statistical properties of STS models and the results in Dickey (1968) to obtain the likelihood function marginally to the variance parameters. This strategy applies under normal-inverted gamma-2 prior distributions for the structural shocks and associated variances. For trend plus noise models such as the local level and the local linear trend, it yields the marginal likelihood by simple or double integration over the (0,1)-support. For trend plus cycle models, we show that marginalizing out the variance parameters greatly improves the accuracy of the Laplace method. We apply this methodology to the analysis of US and euro area NAIRU.

Keywords: Marginal likelihood; Markov Chain Monte Carlo; unobserved components; bridge sampling; Laplace method; NAIRU (search for similar items in EconPapers)
Date: 2008-01
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