Probability Forecast of Downturn in U.S. Economy Using Classical Statistical Decision Theory

This paper presents a paper for producing a probability forecast of a turning point in U.S. economy using Composite Leading Indicators. This methodology is based on classical statistical decision theory and uses information-theoretic measurement to produce a probability. The methodology is flexible using as many historical data points as desired. It is applied to producing probability forecasts of a downturn for 1970-1990 period. Four probability forecasts are produced using different amounts of information. The performance of these forecasts is evaluated using NBER downturn points and the scores measuring accuracy, calibration, and resolution. An indirect comparison of these forecasts with Neftci's sequential probability recursion is also presented. It is shown that the performances of our best two models are statistically different from the performance of the three-consecutive- month decline model and are the same as the one for the probit model. The probit model, however, is more conservative and failed to predict two of the recessions