Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies

Accurate forecasting of the timing and magnitude of macroeconomic recessions caused by unexpected shocks remains an area where both statistical models and judgmental forecasts tend to perform poorly. Inspired by the value-at-risk concept from financial risk management, a growing body of research has been focused on developing a framework to model and quantify macroeconomic risks and estimate the likelihood of adverse macroeconomic outcomes, which has become known as growth-at-risk assessment. The current study proposes an improvement to an established two-step procedure for empirical evaluation of the future growth distribution, which involves directly modelling the parameters of the conditional distribution in one step within an artificial neural network. The proposed procedure is tested on macroeconomic data from four small European open economies covering the coronavirus pandemic lockdown period and the recession related to it. The model achieves a better performance across the four countries compared to the established two-step procedure.