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Journal Article

Cutting-Edge Methods Did Not Improve Inflation Forecasting during the COVID-19 Pandemic


Abstract: Amaze Lusompa and Sai A. Sattiraju investigate whether innovations in time-varying parameter models led to improved inflation forecasting during the pandemic. They find that despite their promise prior to the pandemic, forecasting innovations did not improve the accuracy of inflation forecasts relative to a baseline time-varying parameter model during the pandemic. Their results suggest that forecasters may need to develop a new class of forecasting models, introduce new forecasting variables, or rethink how they forecast to yield more effective inflation forecasts during extreme events.

Keywords: Inflation Forecasting; Time Varying Parameter Models; Pandemic;

JEL Classification: E31;

https://doi.org/10.18651/ER/v107n3LusompaSattiraju

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Bibliographic Information

Provider: Federal Reserve Bank of Kansas City

Part of Series: Economic Review

Publication Date: 2022-07-14

Volume: 107

Issue: no.3