Functional gradient descent (FGD), a recent technique coming from computational statistics, is applied to the estimation of the conditional moments of the short rate process with the goal of finding the main drivers of the drift and volatility dynamics. FGD can improve the accuracy of some reasonable starting estimates obtained using classical short rate models introduced in the literature. It exploits the predictive information of an enlarged set of variables, including yields at other maturities, time, and macroeconomic indicators. Fitting this methodology to the time series of monthly US 3-month Treasury bill rates, we find that the drift dynamics react mostly in a non-linear way to changes in macroeconomic variables, whereas volatility dynamics are subjected to time-dependent regime-switches. Finally we show the superior performance of the final predictions obtained by applying FGD in a forecasting exercis
