Sparse Warcasting

Dados Bibliográficos

ID21393033
AutoresMihnea Constantinescu (0000-0002-2700-2589, University of Amsterdam Amsterdam the Netherlands, autor correspondente)
Ano2026
Volume73
Fascículo3
Data de publicação2026-07-01
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoScottish Journal of Political Economy (JOURNAL)
Identificadores do periódicoISSN: 0036-9292 • E-ISSN: 1467-9485
EditoraWiley (PUBLISHER • GB)
DOI10.1111/sjpe.70058
OpenAlexW7152720009
IdiomaEN
Citações recebidas1
Referências citadas49

Forecasting economic activity during institutional collapse requires nowcasts derived exclusively from alternative data sources. Such sources are abundant yet theoretically unanchored and potentially weakly informative. This study examines whether sparse supervised dimension reduction extracts reliable signals in a context rich in data but poor in statistics. Applying sparse Partial Least Squares to nowcast Ukrainian GDP during the 2022 invasion using only Google search categories, the methodology achieves lower nowcast errors than unsupervised Principal Component Regression. Geographic disaggregation amplifies gains: capital city search data systematically outperforms national aggregates across GDP components, consistent with information centralization in economic centers during existential threats

Component (thermodynamics) · Context (archaeology) · Dimension (graph theory) · Dimensionality reduction · Estimation · Nowcasting · Partial least squares regression · Principal component analysis · Data Quality and Management · Data-Driven Disease Surveillance · Human Mobility and Location-Based Analysis