Probit based time series models in recession forecasting : A survey with an empirical illustration for Finland

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 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.