Recession Forecasting Using Bayesian Classification

We demonstrate the use of a Naive Bayes model as a recession forecasting tool. The approach has a close connection to Markov-switching models and logistic regression but also important differences. In contrast to Markov-switching models, Naive Bayes treats National Bureau of Economic Research business cycle turning points as data rather than hidden states to be inferred by the model. Although Naive Bayes and logistic regression are asymptotically equivalent under certain distributional assumptions, the assumptions do not hold for business cycle data. As a result, Naive Bayes has a larger asymptotic error rate, but converges to the error rate faster than logistic regression, resulting in more accurate recession forecasts with limited data. We show Naive Bayes consistently outperforms logistic regression and the Survey of Professional Forecasters for real-time recession forecasting up to 12 months in advance. These results hold under standard error measures, and also under a novel measure that varies the penalty on false signals depending on when they occur within a cycle. A false signal in the middle of an expansion, for example, is penalized more heavily than one occurring close to a turning point