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 to
estimate the recession probabilities, while others use models based on a latent variable approach
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 outperform
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.