A Bayesian model comparison for trend-cycle decompositions of output

Joshua Chan and Angelia Grant

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

Abstract: We compare a number of widely used trend-cycle decompositions of output in a formal Bayesian model comparison exercise. This is motivated by the often markedly different results from these decompositions—different decompositions have broad implications for the relative importance of real versus nominal shocks in explaining variations in output. Using US quarterly real GDP, we find that the overall best model is an unobserved components model with two features: 1) a nonzero correlation between trend and cycle innovations; 2) a break in output growth in 2007. Under this specification, annualized trend output growth decreases from about 3.4% to 1.5% after the break. The results also indicate that real shocks are more important than nominal shocks.

Keywords: Bayesian model comparison; unobserved components; structural break; business cycle (search for similar items in EconPapers)
JEL-codes: C11 C52 E32 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2015-08
New Economics Papers: this item is included in nep-ets, nep-fdg and nep-mac
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: A Bayesian Model Comparison for Trend‐Cycle Decompositions of Output (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2015-31

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