Improving Recession Probability Forecasts in the U.S. Economy

There are two margins to improve forecasting models of the U.S. recession probability: including additional variables and using a different functional form. Using out-of-sample and cross validation methods, I systematically compare the performance of various forecasting models that differ in terms of variables included and functional forms used. I find substantial gains from including additional variables, such as the S&P 500 and non-farm employment growth, together with the Treasury term spread. In addition, there is a room to further improve forecasting accuracy by utilizing a non-Normal cumulative distribution function. I also examine this possibility by using the generalized Edgeworth expansion, which enables us to explore a wider set of skewness and excess kurtosis. We can obtains further gains by allowing for more flexibility in the functional form.