A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
The Role of Coincident Information in Real‐Time Business Cycle Forecasting
Tekijät: Kuntze, Visa
Kustantaja: Wiley
Julkaisuvuosi: 2026
Lehti: Journal of Forecasting
Artikkelin numero: for.70166
ISSN: 1099-131X
eISSN: 0277-6693
DOI: https://doi.org/10.1002/for.70166
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Osittain avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1002/for.70166
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/523226063
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
Official NBER recession dates are announced with substantial delay. Therefore, real-time forecasters cannot condition on the most recent business cycle states even though recessions and expansions are highly persistent. I study whether real-time coincident releases can substitute for this missing information. At each monthly forecast origin, I construct a recession nowcast, using four coincident indicators and several supervised classifiers, and add this nowcast probability to standard probit forecasting models. In an out-of-sample evaluation for US monthly data from 1986 to 2021, nowcast augmentation improves forecast accuracy at short horizons and at the 1-year horizon relative to a term spread benchmark, while including the raw coincident indicators directly is less effective. The gains are incremental once strong leading indicators are included, and model rankings are sensitive to resampling variation.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
The author thanks Paolo Fornaro, Henri Nyberg, and participants at Helsinki GSE Econometrics Workshop (2023) for useful comments. I gratefully acknowledge the financial support from the Academy of Finland (grant 321968), the Foundation for Economic Education (Liikesivistysrahasto, grant 220246), and the EXACTUS Doctoral Programme at the University of Turku. The author used OpenAI Codex (model gpt-5.1-codex-max) to assist with code development and optimization, and Claude (Anthropic, Sonnet 4.6) to assist with editing and structuring the manuscript text. All research design, analysis, interpretation of results, and final content remain the sole responsibility of the author. AI-generated content was reviewed and verified throughout. Open access publishing facilitated by Turun yliopisto, as part of the Wiley - FinELib agreement.
