SSE/EFI Working Paper Series in Economics and Finance
Gianluigi Rech, Timo Teräsvirta
A simple variable selection technique for nonlinear models
() and Rolf Tschernig
Abstract: 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 Taylor
expansion of the nonlinear model around a given point in the sample space.
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 Taylor expansion is high.
Large samples can be handled without problems.
Keywords: Autoregression; nonlinear regression; nonlinear time series; nonparametric variable selection; time series modelling; (follow links to similar papers)
JEL-Codes: C22; C51; (follow links to similar papers)
13 pages, February 3, 1999, Revised April 6, 2000
- This paper is published as:
Rech, Gianluigi, Timo Teräsvirta and Rolf Tschernig, (2001), 'A simple variable selection technique for nonlinear models', Communications in Statistics, Theory and Methods, Vol. 30, pages 1227-1241
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