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Linearity Testing Against a Fuzzy Rule-based Model

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Author Info

  • José Luis Aznarte

    ()
    (Department of Computer Science and Artificial Intelligence,CITIC-UGR, University of Granada, (Spain))

  • Marcelo Cunha Medeiros

    ()
    (Department of Economics PUC-Rio)

  • José Manuel Benítez Sánchez

    (Department of Computer Science and Artificial Intelligence,CITIC-UGR, University of Granada, (Spain))

Abstract

In this paper, we introduce a linearity test for fuzzy rule-based models in the framework of time series modeling. To do so, we explore a family of statistical models, the regime switching autoregressive models, and the relations that link them to the fuzzy rule-based models. From these relations, we derive a Lagrange Multiplier linearity test and some properties of the maximum likelihood estimator needed for it. Finally, an empirical study of the goodness of the test is presented.

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Bibliographic Info

Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 566.

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Length: 24p
Date of creation: Mar 2010
Date of revision:
Handle: RePEc:rio:texdis:566

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Related research

Keywords: fuzzy rule-based models; time series; linearity test; statistical inference;

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References

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  1. 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).
  2. Medeiros, Marcelo C. & Teräsvirta, Timo & Rech, Gianluigi, 2002. "Building neural network models for time series: A statistical approach," Working Paper Series in Economics and Finance 508, Stockholm School of Economics.
  3. White, Halbert & Domowitz, Ian, 1984. "Nonlinear Regression with Dependent Observations," Econometrica, Econometric Society, vol. 52(1), pages 143-61, January.
  4. Potscher, Benedikt M. & Prucha, Ingmar R., 1986. "A class of partially adaptive one-step m-estimators for the non-linear regression model with dependent observations," Journal of Econometrics, Elsevier, vol. 32(2), pages 219-251, July.
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