Working Paper: CEPR ID: DP16760
Authors: Frank Schorfheide; Dongho Song
Abstract: We resuscitated the mixed-frequency vector autoregression (MF-VAR) developed in Schorfheide and Song (2015, JBES) to generate macroeconomic forecasts for the U.S. during the COVID-19 pandemic in real time. The model combines eleven time series observed at two frequencies: quarterly and monthly. We deliberately did not modify the model specification in view of the COVID-19 outbreak, except for the exclusion of crisis observations from the estimation sample. We compare the MF-VAR forecasts to the median forecast from the Survey of Professional Forecasters (SPF). While the MF-VAR performed poorly during 2020:Q2, subsequent forecasts were at par with the SPF forecasts. We show that excluding a few months of extreme observations is a promising way of handling VAR estimation going forward, as an alternative of a sophisticated modeling of outliers.
Keywords: Bayesian inference; COVID-19; Macroeconomic forecasting; Minnesota prior; Realtime data; Survey of professional forecasters; Vector autoregressions
JEL Codes: C11; C32; C53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Exclusion of extreme observations (C46) | Improved forecasting accuracy (C53) |
| Poor performance of mfvar model in 2020Q2 (C22) | Overly pessimistic forecasts (G17) |
| Lack of information regarding pandemic's severity (D89) | Poor performance of mfvar model in 2020Q2 (C22) |
| Model adjustments (C51) | Forecasting outcomes (C53) |
| mfvar forecasts from July 2020 onwards (E17) | Accurate and comparable to SPF forecasts (C53) |