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

Amaze Lusompa and Sai Sattiraju

Economic Review, 2022, vol. 107, issue no.3

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 (search for similar items in EconPapers)
JEL-codes: E31 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.18651/ER/v107n3LusompaSattiraju

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