A simple variable selection technique for nonlinear models

Gianluigi Rech, Timo Teräsvirta and Rolf Tschernig ()

No 1999,26, SFB 373 Discussion Papers from Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes

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 an adequate Taylor expansion is of high order. Large samples can be handled without problems.

Keywords: nonlinear regression; Autoregression; nonlinear time series; nonparametric variable selection; time series modelling (search for similar items in EconPapers)
Date: 1999
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Citations: View citations in EconPapers (17)

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Working Paper: A simple variable selection technique for nonlinear models (2000)
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