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A simple variable selection technique for nonlinear models

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Author Info

  • Rech, Gianluigi
  • Teräsvirta, Timo
  • Tschernig, Rolf

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. --

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Bibliographic Info

Paper provided by Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes in its series SFB 373 Discussion Papers with number 1999,26.

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Date of creation: 1999
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Handle: RePEc:zbw:sfb373:199926

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Related research

Keywords: nonlinear regression; Autoregression; nonlinear time series; nonparametric variable selection; time series modelling;

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References

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  1. Tschernig, Rolf & Yang, Lijian, 1997. "Nonparametric lag selection for time series," SFB 373 Discussion Papers 1997,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
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Cited by:
  1. Leila Ali & Marie Lebreton, 2013. "The Fall of Bretton Woods: Which Geography Matters?," Economics Bulletin, AccessEcon, vol. 33(2), pages 1396-1419.
  2. Timo Teräsvirta & Dick van Dijk & Marcelo Cunha Medeiros, 2004. "Linear models, smooth transition autoregressions and neural networks for forecasting macroeconomic time series: A reexamination," Textos para discussão 485, Department of Economics PUC-Rio (Brazil).
  3. Eduardo Mendes & Alvaro Veiga & MArcelo Cunha Medeiros, 2007. "Estimation And Asymptotic Theory For A New Class Of Mixture Models," Textos para discussão 538, Department of Economics PUC-Rio (Brazil).
  4. 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.
  5. Mayte Suarez Farinãs & Carlos Eduardo Pedreira & Marcelo C. Medeiros, 2003. "Local-global neural networks: a new approach for nonlinear time series modelling," Textos para discussão 470, Department of Economics PUC-Rio (Brazil).
  6. 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.
  7. Marie Lebreton & Katia Melnik, 2009. "Voluntary Participation as a Determinant of Social Capital in France : Allowing for Parameter Heterogeneity," Working Papers halshs-00410530, HAL.
  8. 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).
  9. MArcelo C. Medeiros & Eduardo F.Mendes, 2012. "Estimating High-Dimensional Time Series Models," Textos para discussão 602, Department of Economics PUC-Rio (Brazil).
  10. McAleer, Michael & Medeiros, Marcelo C., 2008. "A multiple regime smooth transition Heterogeneous Autoregressive model for long memory and asymmetries," Journal of Econometrics, Elsevier, vol. 147(1), pages 104-119, November.
  11. Barrera, Carlos R., 2010. "Redes neuronales para predecir el tipo de cambio diario," Working Papers 2010-001, Banco Central de Reserva del Perú.

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