Sparse warcasting - Mihnea Constantinescu (Graduate Institute of International and Development Studies, 2023)
Forecasting economic activity during an invasion is a nontrivial exercise. The lack of timely statistical data and the expected nonlinear effect of military action challenge the use of established nowcasting and shortterm forecasting methodologies. In a recent study (Constantinescu (2023b)), I explore the use of Partial Least Squares (PLS) augmented with an additional variable selection step to nowcast quarterly Ukrainian GDP using Google search data. Model outputs are benchmarked against both static and Dynamic Factor Models. Preliminary results outline the usefulness of PLS in capturing the effects of large shocks in a setting rich in data, but poor in statistics.
Keywords: C38; C53; 55; E32; E37; 330; Nowcasting; quarterly GDP; Google Trends; Machine Learning; Partial Least Squares; Sparsity; Markov Blanket