Learning-Based Inflation Expectations in an Unobserved Components Model

28 Pages Posted: 6 Apr 2021

See all articles by Irina Panovska

Irina Panovska

University of Texas at Dallas

Srikanth Ramamurthy

affiliation not provided to SSRN

Date Written: April 2, 2021

Abstract

We examine the role of adaptive learning-based inflation expectations in determining the output gap within the context of an Unobserved Components model. The forward-looking New Keynesian Phillips curve serves as the backbone for modeling inflation dynamics. We find that learning based inflation forecasts largely shadow survey expectations in the pre-Volcker era and they do not exhibit persistent overshooting during the initial stages of the financial crisis. Likewise, our implied output gap also deviates the most from a gap estimated using survey expectations in the post 1984 sample. The interesting learning dynamics around business cycle turning points during this period indicate that the last three recessions 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, Learning-Based Inflation Expectations in an Unobserved Components Model (April 2, 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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