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The authors document the impact of COVID-19 on frequently employed time series models. They show that for both single equation models (Phillips curves) and Vector Autoregressions (VARs) estimated parameters change notably with the pandemic. In a VAR, allowing the errors to have a distribution with fatter tails than the Gaussian one equips the model to better deal with the COVID-19 shock. A standard Gaussian VAR can still be used for producing conditional forecasts when relevant off-model information is used. The authors illustrate this by conditioning on official projections for a set of variables, but also by tilting to expectations from the Survey of Professional Forecasters. For Phillips curves, averaging across many conditional forecasts in a thick modelling framework offers some hedge against parameter instability.
Elena Bobeica: Elena Bobeica is a Principal Economist in the Prices and Costs Division of the European Central Bank. She previously worked in the Research Directorate of the ECB, as well as in academics. She has a vast experience working in coordination teams of expert groups within the European System of Central Bank on understanding drivers of inflation or on the functioning of the price and wage Phillips curve in euro area. Her research is focusing on modelling and forecasting inflation and on drivers of the pass-through from wages to inflation.
Benny Hartwig: Benny Hartwig is a Research Analyst in the Prices and Costs Division in the Directorate General Economics at the European Central Bank. Prior to this he was Research Assistant in the Research Centre at the Deutsche Bundesbank and Teaching Assistant for the Chair of International Macroeconomics and Macroeconometrics of Professor Michael Binder PhD at Goethe University. Benny is a PhD candidate in Economics at the Graduate School of Economics, Finance and Management of Goethe University Frankfurt. His research focuses on Bayesian econometrics, monetary policy and systemic risk.
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