Economics at your fingertips  

Sparse Warcasting

Mihnea Constantinescu ()
Additional contact information
Mihnea Constantinescu: National Bank of Ukraine

No 01/2023, Working Papers from National Bank of Ukraine

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 short-term forecasting methodologies. This study explores the use of Partial Least Squares (PLS) augmented with an additional sparsity 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: 2023-06
New Economics Papers: this item is included in nep-big, nep-cis and nep-tra
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link) (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Working Papers from National Bank of Ukraine Contact information at EDIRC.
Bibliographic data for series maintained by Research Unit ().

Page updated 2023-09-24
Handle: RePEc:ukb:wpaper:01/2023