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A simple variable selection technique for nonlinear models

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Author Info
Rech, Gianluigi (Dept. of Economic Statistics, Stockholm School of Economics)
Teräsvirta, Timo () (Dept. of Economic Statistics, Stockholm School of Economics)
Tschernig, Rolf (Institut für Statistik und Ökonometrie)

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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.

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Publisher Info
Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 296.

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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.
Handle: RePEc:hhs:hastef:0296

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Related research
Keywords: Autoregression nonlinear regression nonlinear time series nonparametric variable selection time series modelling

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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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  1. Marcelo Cunha Medeiros & Álvaro Veiga & Carlos Eduardo Pedreira, 2000. "Modelling exchange rates: smooth transitions, neural networks, and linear models," Textos para discussão 432, Department of Economics PUC-Rio (Brazil). [Downloadable!]
  2. Marcelo C. Medeiros & Timo Terasvirta, 2001. "Statistical methods for modelling neural networks," Textos para discussão 445, Department of Economics PUC-Rio (Brazil). [Downloadable!]
  3. Medeiros, Marcelo & Veiga, Alvaro, 2000. "Diagnostic Checking in a Flexible Nonlinear Time Series Model," Working Paper Series in Economics and Finance 386, Stockholm School of Economics, revised 15 Jan 2001.
    Other versions:
  4. Teräsvirta, Timo & van Dijk, Dick & Medeiros, Marcelo, 2004. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," Working Paper Series in Economics and Finance 561, Stockholm School of Economics, revised 04 Nov 2004.
    Other versions:
  5. Marcelo C. Medeiros & Timo Terasvirta & Gianluigi Rech, 2002. "Building Neural Network Models for Time Series: A Statistical Approach," Textos para discussão 461, Department of Economics PUC-Rio (Brazil). [Downloadable!]
    Other versions:
  6. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 10 Feb 2000.
  7. Anne Péguin-Feissolle & Birgit Strikholm & Timo Teräsvirta, 2008. "Testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form," CREATES Research Papers 2008-19, School of Economics and Management, University of Aarhus. [Downloadable!]
    Other versions:
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