Predicting recessions using trends in the yield spread

The yield spread, measured as the difference between long- and short-term interest rates, is widely regarded as one of the strongest predictors of economic recessions. In this paper, we propose an enhanced recession prediction model that incorporates trends in the value of the yield spread. We expec...

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Bibliographic Details
Published in:Journal of applied statistics 2019-05, Vol.46 (7), p.1323-1335
Main Authors: Kozlowski, Steven E., Sim, Thaddeus
Format: Article
Language:English
Subjects:
Artificial neural networks
classification
cross-validation
Economic models
Equity indexed annuities
Forecasting
Interest rates
logistic regression
Neural networks
Prediction models
Recessions
Regression models
Statistical methods
Trends
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Summary:The yield spread, measured as the difference between long- and short-term interest rates, is widely regarded as one of the strongest predictors of economic recessions. In this paper, we propose an enhanced recession prediction model that incorporates trends in the value of the yield spread. We expect our model to generate stronger recession signals because a steadily declining value of the yield spread typically indicates growing pessimism associated with the reduced future business activity. We capture trends in the yield spread by considering both the level of the yield spread at a lag of 12 months as well as its value at each of the previous two quarters leading up to the forecast origin, and we evaluate its predictive abilities using both logit and artificial neural network models. Our results indicate that models incorporating information from the time series of the yield spread correctly predict future recession periods much better than models only considering the spread value as of the forecast origin. Furthermore, the results are strongest for our artificial neural network model and logistic regression model that includes interaction terms, which we confirm using both a blocked cross-validation technique as well as an expanding estimation window approach.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2018.1537364