Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/224447 
Authors: 
Year of Publication: 
2020
Series/Report no.: 
BoF Economics Review No. 7/2020
Publisher: 
Bank of Finland, Helsinki
Abstract: 
This article surveys both earlier and recent research on recession forecasting with probit based time series models. Most studies use either a static probit model or its extensions in order toestimate the recession probabilities, while others use models based on a latent variable ap-proach to account for nonlinearities. Many studies find that the term spread (i.e, the difference between long-term and short-term yields) is a useful predictor for recessions, but some recent studies also find that the ability of spread to predict recessions in the Euro Area has diminished over the years. Confidence indicators and financial variables such as stock returns seem to provide additional predictive power over the term spread. More sophisticated models outper-form the basic static probit model in various studies. An empirical analysis made for Finland strengthens the findings of earlier studies. Consumer confidence is especially useful predictor of Finnish business cycle and the accuracy of the static single-predictor model can be improved by using multiple predictors and by allowing the dynamic extension.
Subjects: 
business cycles
recession forecasting
probit models
Persistent Identifier of the first edition: 
Document Type: 
Research Report

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