Decomposing the Output Gap with Inflation Learning

33 Pages Posted: 6 Apr 2021 Last revised: 24 May 2021

See all articles by Irina Panovska

Irina Panovska

University of Texas at Dallas

Srikanth Ramamurthy

affiliation not provided to SSRN

Date Written: May 23, 2021

Abstract

We incorporate adaptive learning-based inflation expectations in an Unobserved Components model in order to study the link between inflation and the output gap. The forward-looking New Keynesian Phillips curve serves as the backbone for modeling inflation dynamics. We find that learning based inflation forecasts not only shadow survey expectations in the pre-Volcker era, they also do not exhibit the persistent overshooting during the initial stages of the financial crisis that is seen with surveys. The resulting output gap from our model has a lower amplitude than the gap estimated using survey expectations in the post 1984 sample. The interesting learning dynamics around business cycle turning points indicate that several recessions, including the recent Great Recession, were at least partially driven by large drops in the trend component of output.

Keywords: Adaptive Learning, Output Gap, Inflation, Unobserved Components Model

JEL Classification: E31, E32, E50, C32

Suggested Citation

Panovska, Irina and Ramamurthy, Srikanth, Decomposing the Output Gap with Inflation Learning (May 23, 2021). Available at SSRN: https://ssrn.com/abstract=3818460 or http://dx.doi.org/10.2139/ssrn.3818460

Irina Panovska (Contact Author)

University of Texas at Dallas ( email )

2601 North Floyd Road
Richardson, TX 75083
United States

Srikanth Ramamurthy

affiliation not provided to SSRN

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