Similarity-based path forecasting of US recession periods

Bibliographic Data

ID21542494
AuthorsVisa Kuntze (0000-0002-4372-1647, University of Turku, corresponding author), Henri Nyberg (0000-0002-9160-1818, University of Turku), Samuel Rauhala (University of Turku)
Year2026
Volume70
Issue3
Publication date2026-03-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueEmpirical Economics (JOURNAL)
Journal identifiersISSN: 0377-7332 • E-ISSN: 1435-8921
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s00181-026-02893-7
OpenAlexW7130652612
LanguageEN
References cited14

We develop a nonparametric similarity-based approach for binary time series that exploits recurring historical patterns to construct probability forecasts for all feasible multi-period outcome sequences. In contrast to conventional horizon-specific parametric models, our path forecasts are obtained simultaneously for all the horizons and remain internally consistent across them. Simulation experiments demonstrate that our method delivers accurate and robust performance in realistic sample sizes. In an empirical application to US business cycle data, our approach successfully anticipates the onset of the past three recessions about one year in advance and provides informative predictions of their expected duration

Business cycle · Nonparametric statistics · Parametric statistics · Recession · Financial Risk and Volatility Modeling · Forecasting Techniques and Applications · Stock Market Forecasting Methods

Tools

Open DOIOpen Access