Predicting U.s. Recessions With Dynamic Binary response Models

摘要

We develop dynamic binary probit models and apply them for predicting U.S. recessions using the interest rate spread as the driving predictor. The new models use lags of the binary response (a recession dummy) to forecast its future values and allow for the potential forecast power of lags of the underlying conditional probability. We show how multiperiod-ahead forecasts are computed iteratively using the same one-period-ahead model. Iterated forecasts that apply specific lags supported by statistical model selection procedures turn out to be more accurate than previously used direct forecasts based on horizon-specific model specifications.