Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/283386 
Year of Publication: 
2023
Series/Report no.: 
Graduate Institute of International and Development Studies Working Paper No. HEIDWP15-2023
Publisher: 
Graduate Institute of International and Development Studies, Geneva
Abstract: 
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.
Subjects: 
Nowcasting
quarterly GDP
Google Trends
Machine Learning
Partial Least Squares
Sparsity
Markov Blanket
JEL: 
C38
C53
55
E32
E37
Document Type: 
Working Paper

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