Nonparametric prediction in time series analysis: Some empirical results

Abstract

In this paper a new approach to select the lag p for time series generated from Markov processes is proposed. It is faced in the nonparametric domain and it is based on the minimisation of the estimated risk of prediction of one-step-ahead kernel predictors. The proposed procedure has been evaluated through a Monte Carlo study and in empirical context to forecast the weakly 90-day US T-bill secondary market rates.