Sparse Warcasting

Datos Bibliográficos

ID21393033
AutoresMihnea Constantinescu (0000-0002-2700-2589, University of Amsterdam Amsterdam the Netherlands, autor de correspondencia)
Año2026
Volumen73
Número3
Fecha de publicación2026-07-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaScottish Journal of Political Economy (JOURNAL)
Identificadores de la revistaISSN: 0036-9292 • E-ISSN: 1467-9485
EditorialWiley (PUBLISHER • GB)
DOI10.1111/sjpe.70058
OpenAlexW7152720009
IdiomaEN
Citas recibidas1
Referencias 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