Measuring the output gap using stochastic model specification search

Joshua Chan and Angelia Grant

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

Abstract: It is well known that different specification choices can give starkly different output gap estimates. To account for model uncertainty, we average estimates over a wide variety of popular specifications using stochastic model specification search. In particular, we consider three types of specification choices: sets of variables used in the analysis, output trend specifications and distributional assumptions. Using US data, we find that the unemployment gap is useful in estimating the output gap, but conditional on the unemployment gap, the inflation gap no longer depends on the output gap. Our results show a steady decline in trend output growth throughout the sample, and the estimate at the end of our sample is only about 1%. Moreover, data favor t over Gaussian distributed innovations, suggesting the relatively frequent occurrence of extreme events.

Keywords: model averaging; trend inflation; potential output; NAIRU; Okun’s law; Phillips curve (search for similar items in EconPapers)
JEL-codes: C11 C52 E32 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2017-01
New Economics Papers: this item is included in nep-ecm, nep-mac and nep-ore
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:een:camaaa:2017-02

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