Journal
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS
Volume 30, Issue 6, Pages 1227-1241Publisher
MARCEL DEKKER INC
DOI: 10.1081/STA-100104360
Keywords
autoregression; nonlinear regression; nonlinear time series; nonparametric variable selection; time series modelling
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Applying nonparametric variable selection criteria in nonlinear regression models generally requires a substantial computational effort if the data set is large. In this paper we present a selection technique that is computationally much less demanding and performs well in comparison with methods currently available. It is based on a polynomial approximation of the nonlinear model. Performing the selection only requires repeated least squares estimation of models that are linear in parameters. The main limitation of the method is that the number of variables among which to select cannot be very large if the sample is small and the order of an adequate polynomial at the same time is high. Large samples can be handled without problems.
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