A1 Refereed original research article in a scientific journal
Similarity-based path forecasting of US recession periods
Authors: Kuntze, Visa; Nyberg, Henri; Rauhala, Samuel
Publisher: Springer Nature
Publication year: 2026
Journal: Empirical Economics
Article number: 52
Volume: 70
Issue: 3
ISSN: 0377-7332
eISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-026-02893-7
Publication's open availability at the time of reporting: Open Access
Publication channel's open availability : Partially Open Access publication channel
Web address : https://doi.org/10.1007/s00181-026-02893-7
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/515796509
Self-archived copy's licence: CC BY
Self-archived copy's version: Publisher`s PDF
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.
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Funding information in the publication:
Open Access funding provided by University of Turku (including Turku University Central Hospital).
