Agnostic Output Gap Estimation and Decomposition in Large Cross-Sections

Florian Eckert and Samad Sarferaz ()
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Samad Sarferaz: KOF Swiss Economic Institute, ETH Zurich, Switzerland

No 19-467, KOF Working papers from KOF Swiss Economic Institute, ETH Zurich

Abstract: This paper uses a Bayesian non-stationary dynamic factor model to extract common trends and cycles from large datasets. An important but neglected feature of Bayesian statistics allows to treat stationary and non-stationary time series equally in terms of parameter estimation. Based on this feature we show how to extract common trends and cycles from the data by ex-post processing the posterior output and describe how to derive an agnostic output gap measure. We apply the procedure to a large panel of quarterly time series that covers 158 macroeconomic and financial series for the United States. We find that our derived output gap measure tracks the U.S. business cycle well, exhibiting a high correlation with alternative estimates of the output gap. Since the factors are extracted from a comprehensive dataset, the resulting output gap estimates are stable at the current edge and can be decomposed in a new and meaningful way.

Keywords: Non-Stationary Dynamic Factor Model; Potential Output Estimation; Output Gap Decomposition (search for similar items in EconPapers)
JEL-codes: C11 C32 E32 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2019-12
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-mac
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Persistent link: https://EconPapers.repec.org/RePEc:kof:wpskof:19-467

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