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Local-global neural networks: a new approach for nonlinear time series modelling

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Author Info

  • Mayte Suarez Farinãs
  • Carlos Eduardo Pedreira
  • Marcelo C. Medeiros

    () (Department of Economics PUC-Rio)

Abstract

In this paper, the Local Global Neural Networks model is proposed within the context of time series models. This formulation encompasses some already existing nonlinear models and also admits the Mixture of Experts approach. We place emphasis on the linear expert case and extensively discuss the theoretical aspects of the model: stationarity conditions, existence, consistency and asymptotic normality of the parameter estimates, and model identifiability. A model building strategy is also considered and the whole procedure is illustrated with two real time-series.

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File URL: ftp://139.82.198.57/pdf/td470.pdf
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Bibliographic Info

Paper provided by Department of Economics PUC-Rio (Brazil) in its series Textos para discussão with number 470.

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Length: 38 pages
Date of creation: Oct 2003
Date of revision:
Publication status: Published in the Journal of the American Statistical Association, v.99, n. 468, p. 1092-1107, 2004
Handle: RePEc:rio:texdis:470

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Keywords: neural networks; nonlinear models; time-series; model identifiability; parameter estimation; model building; sunspot number.;

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Cited by:
  1. Chan, F. & McAleer, M.J. & Medeiros, M.C., 2011. "Structure and Asymptotic theory for Nonlinear Models with GARCH Errors," Econometric Institute Report EI 2010-79, Erasmus University Rotterdam, Econometric Institute.
  2. Medeiros, Marcelo C. & McAleer, Michael & Slottje, Daniel & Ramos, Vicente & Rey-Maquieira, Javier, 2008. "An alternative approach to estimating demand: Neural network regression with conditional volatility for high frequency air passenger arrivals," Journal of Econometrics, Elsevier, vol. 147(2), pages 372-383, December.
  3. Areosa, Waldyr Dutra & McAleer, Michael & Medeiros, Marcelo C., 2011. "Moment-based estimation of smooth transition regression models with endogenous variables," Journal of Econometrics, Elsevier, vol. 165(1), pages 100-111.
  4. Stockis, Jean-Pierre & Tadjuidje-Kamgaing, Joseph & Franke, Jürgen, 2008. "A note on the identifiability of the conditional expectation for the mixtures of neural networks," Statistics & Probability Letters, Elsevier, vol. 78(6), pages 739-742, April.
  5. 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.
  6. José Luis Aznarte & Marcelo Cunha Medeiros & José Manuel Benítez Sánchez, 2010. "Linearity Testing Against a Fuzzy Rule-based Model," Textos para discussão 566, Department of Economics PUC-Rio (Brazil).
  7. Eric Hillebrand & Marcelo C. Medeiros & Junyue Xu, 2012. "Asymptotic Theory for Regressions with Smoothly Changing Parameters," CREATES Research Papers 2012-31, School of Economics and Management, University of Aarhus.
  8. McAleer, Michael & Medeiros, Marcelo C. & Slottje, Daniel, 2008. "A neural network demand system with heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 147(2), pages 359-371, December.

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