Can Machine Learning Catch the COVID-19 Recession?

Forecasting economic developments during crisis time is problematic since the realizations of the variables are far away from their average values, while econometric models are typically better at explaining and predicting values close to the average, particularly so in the case of linear models. The situation is even worse for the Covid-19 induced recession, when typically well performing econometric models such as Bayesian VARs with stochastic volatility have troubles in tracking the unprecedented fall in real activity and labour market indicators -- see for example for the US Carriero et al. (2020) and Plagborg-Møller et al. (2020), or An and Loungani (2020) for an analysis of the past performance of the Consensus Forecasts. As a partial solution, Foroni et al. (2020) employ simple mixed-frequency models to nowcast and forecast US and the rest of G7 GDP quarterly growth rates, using common monthly indicators, such as industrial production, surveys, and the slope of the yield curve. They then adjust the forecasts by a specific form of intercept correction or estimate by the similarity approach, see Clements and Hendry (1999) and Dendramis et al. (2020), showing that the former can reduce the extent of the forecast error during the Covid-19 period. Schorfheide and Song (2020) do not include COVID periods in the estimation of a mixed-frequency VAR model because those observations substantially alter the forecasts. An alternative approach is the specification of sophisticated nonlinear / time-varying models. While this is not without perils when used on short economic time series, it can yield some gains, see e.g.