Finite Sample Performances of the Model Selection Approach in Nonparametric Model Specification for Time Series

Abstract

Nonparametric model specification for stationary time series involves selections of the smoothing parameter (bandwidth), the lag structure and the functional form (linear vs. nonlinear). In real life problems, none of these factors are known and the choices are interdependent. In this article, we recommend to accomplish these choices in one step via the model selection approach. Two procedures are considered; one based on the information criterion and the other based on the least squares cross validation. The Monte Carlo simulation results show that both procedures have good finite sample performances and are easy to implement compared to existing two-step probabilistic testing procedures.