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Identification of non-linear additive autoregressive models

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We propose a lag selection method for non-linear additive autoregressive models that is based on spline estimation and the Bayes information criterion. The additive structure of the autoregression function is used to overcome the ‘curse of dimensionality’, whereas the spline estimators effectively take into account such a structure in estimation. A stepwise procedure is suggested to implement the method proposed. A comprehensive Monte Carlo study demonstrates good performance of the method proposed and a substantial computational advantage over existing local-polynomial-based methods. Consistency of the lag selection method based on the Bayes information criterion is established under the assumption that the observations are from a stochastic process that is strictly stationary and strongly mixing, which provides the first theoretical result of this kind for spline smoothing of weakly dependent data.

Keywords: Bayes information criterion; Lag selection; Non-linear time series; Nonparametric method; Splines; Stochastic regression; Variable selection

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/j.1369-7412.2004.05500.x

Affiliations: 1: University of Pennsylvania, Philadelphia, USA. 2: Michigan State University, East Lansing, USA

Publication date: April 1, 2004

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