, Timo Teräsvirta
() and Rolf Tschernig
Gianluigi Rech: Dept. of Economic Statistics, Stockholm School of Economics, Postal: P.O. Box 6501, SE-113 83 Stockholm, Sweden
Timo Teräsvirta: Dept. of Economic Statistics, Stockholm School of Economics, Postal: P.O. Box 6501, SE-113 83 Stockholm, Sweden
Rolf Tschernig: Institut für Statistik und Ökonometrie, Postal: Humboldt-Universität zu Berlin, Spandauer Str. 1, D-10178 Berlin, Germany
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.
13 pages, First version: February 3, 1999. Revised: April 6, 2000.
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