SSE/EFI Working Paper Series in Economics and Finance,
Stockholm School of Economics

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

JEL-codes: C22; C51

13 pages, First version: February 3, 1999. Revised: April 6, 2000.

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Published as

Gianluigi Rech, 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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