Neural Networks, the Treasury Yield Curve, and Recession Forecasting

The authors use neural networks to examine the power of Treasury term spreads and other macro-financial variables to forecast US recessions and compare them with probit regression. They propose a novel three-step econometric method for cross-validating and conducting statistical inference on machine learning classifiers and explaining forecasts. They find that probit regression does not underperform a neural network classifier in the present application, which stands in contrast to a growing body of literature demonstrating that machine learning methods outperform alternative classification algorithms. That said, neural network classifiers do identify important features of the joint distribution of recession over term spreads and other macro-financial variables that probit regression cannot. The authors discuss some possible reasons for their results and use their procedure to study US recessions over the post-Volcker period, analyzing feature importance across business cycles. TOPICS: Fixed income and structured finance, big data/machine learning, financial crises and financial market history Key Findings ▪ It is difficult to make the case that neural network classifiers greatly outperform traditional econometric methods such as probit regression when forecasting US recessions if performance is measured on the basis of forecast accuracy alone. ▪ That said, neural network classifiers identify important features of the joint distribution of recession over term spreads and other macro-financial time series that probit regression and other traditional methods cannot. ▪ The authors propose a three-step econometric process for conducting statistical inference on machine learning classifiers, with the goal of better explaining recession forecasts and enabling model outputs to be linked back to the instruments of monetary policy.