Forecasting the FOMC's interest rate setting behavior: a further analysis

We develop a model to forecast the Federal Open Market Committee's (FOMC's) interest rate setting behavior in a nonstationary discrete choice model framework by Hu and Phillips (2004). We find that if the model selection criterion is strictly empirical, correcting for nonstationarity is extremely important, whereas it may not be an issue if one has an a priori model. Evaluating an array of models in terms of their out-of-sample forecasting ability, we find that those favored by the in-sample criteria perform worst, while theory-based models perform best. We find the best model for forecasting the FOMC's behavior is a forward-looking Taylor rule model.