A simple variable selection technique for nonlinear models
AbstractApplying 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.
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Bibliographic InfoPaper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 296.
Length: 13 pages
Date of creation: 03 Feb 1999
Date of revision: 06 Apr 2000
Publication status: Published in Communications in Statistics, Theory and Methods, 2001, pages 1227-1241.
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Autoregression; nonlinear regression; nonlinear time series; nonparametric variable selection; time series modelling;
Other versions of this item:
- Rech, Gianluigi & Teräsvirta, Timo & Tschernig, Rolf, 1999. "A simple variable selection technique for nonlinear models," SFB 373 Discussion Papers 1999,26, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
This paper has been announced in the following NEP Reports:
- NEP-ALL-1999-02-08 (All new papers)
- NEP-ECM-1999-02-08 (Econometrics)
- NEP-ETS-1999-02-08 (Econometric Time Series)
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