Global and local components of output gaps

Florian Eckert and Nina Mühlebach ()
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Florian Eckert: ETH Zurich
Nina Mühlebach: ETH Zurich

Empirical Economics, 2023, vol. 65, issue 5, No 11, 2331 pages

Abstract: Abstract This paper proposes a multi-level dynamic factor model to identify common components in output gap estimates. We pool multiple estimates for 157 countries and decompose them into one global, eight regional, and 157 country-specific cycles. Our approach easily deals with mixed frequencies, ragged edges, and discontinuities in the underlying output gap estimates. To restrict the parameter space in the Bayesian state space model, we apply a stochastic search variable selection approach and base the prior inclusion probabilities on spatial information. Our results suggest that the global and the regional cycles explain a substantial proportion of the output gaps. On average, 18% of a country’s output gap is attributable to the global cycle, 24% to the regional cycle, and 58% to the local cycle.

Keywords: Multi-level DFM; Bayesian state space model; Output gap decomposition; Model combination; Business cycles; Variable selection; Spatial Prior (search for similar items in EconPapers)
JEL-codes: C11 C32 C52 F44 R11 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00181-023-02419-5

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