We are pleased to announce that Dr. Irina Panovska (Associate Professor of Economics, The University of Texas at Dallas) will workshop her working paper at the ERG Workshop series on April 5, 2024, at 3pm-4pm (Central).

Topic: Estimated Output Gap in a Wage-Inflation Expectations Model

Abstract: We study how incorporating adaptive learning-based inflation expectations for price and wage inflation can improve the performance of Unobserved Components (UC) models both when it comes to inference about the output gap and when it comes to forecasting performance. We incorporate learning-based expectations and bivariate feedback in the learning dynamics for wage and price inflation, and we augment a conventional reduced-form UC model for output and unemployment with this bivariate learning process. Our model directly integrates the expectations dynamics of the Hybrid New Keynesian Philips Curve while also retaining the appealing statistical features of the UC framework, allowing us to extract information about the output gap. Three interesting sets of results stand out. First, while the perceived persistence of inflation fell during the early stages of the pandemic, it increased sharply and substantially during the period 2021Q2-2022Q2. Second, the estimated output gap started decreasing in mid-2019, decreased sharply during the early stages of the pandemic, and bounced back rapidly. Finally, including information about the output gap and about the price and wage inflation expectations process helps improve macroeconomic forecasts.

Location: WH 312