Out‐of‐Sample Forecasts and Nonlinear Model Selection with an Example of the Term Structure of Interest Rates

Abstract

It is well known that goodness‐of‐fit measures lead to overfitting. We compare the small‐sample properties of linear and several nonlinear models using a Monte Carlo study. A large number of linear series are generated and conventional methods of fitting nonlinear models are applied to each. The best linear and nonlinear models are compared using in‐sample and out‐of‐sample criteria. Out‐of‐sample forecasts are shown to be superior for selecting the proper specification. The experiment is repeated using a nonlinear model and the in‐sample lit and forecasts of the various models are compared. An example is provided using the term structure of interest rates.