Boosting nonlinear additive autoregressive time series

Authors:
Nivien Shafik;Gerhard Tutz
Affiliations:
Institute for Statistics, Seminar for Applied Stochastics, Ludwig-Maximilians-University Munich, Akademiestr. 1, 80799 Munich, Germany;Institute for Statistics, Seminar for Applied Stochastics, Ludwig-Maximilians-University Munich, Akademiestr. 1, 80799 Munich, Germany
Venue:
Computational Statistics & Data Analysis
Year:
2009

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Abstract

Several methods for the analysis of nonlinear time series models have been proposed. As in linear autoregressive models the main problems are model identification, estimation and prediction. A boosting method is proposed that performs model identification and estimation simultaneously within the framework of nonlinear autoregressive time series. The method allows one to select influential terms from a large number of potential lags and exogenous variables. The influence of the selected terms is modeled by an expansion in basis function allowing for a flexible additive form of the predictor. The approach is very competitive in particular in high dimensional settings where alternative fitting methods fail. This is demonstrated by means of simulations and two applications to real world data.