Sparse Warcasting

Mihnea Constantinescu
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Mihnea Constantinescu: National Bank of Ukraine and University of Amsterdam

No 15-2023, IHEID Working Papers from Economics Section, The Graduate Institute of International Studies

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 this study 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: Nowcasting; quarterly GDP; Google Trends; Machine Learning; Partial Least Squares; Sparsity; Markov Blanket (search for similar items in EconPapers)
JEL-codes: C38 C53 C55 E32 E37 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2023-09-14, Revised 2023-10-02
New Economics Papers: this item is included in nep-big, nep-cis, nep-ets, nep-ger and nep-tra
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Persistent link: https://EconPapers.repec.org/RePEc:gii:giihei:heidwp15-2023

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