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Statistical methods for modelling neural networks

Author

Listed:
  • Marcelo C. Medeiros

    (Department of Economics PUC-Rio)

  • Timo Terasvirta

    (Department of Economic Statistics, Stockholm School of Economics)

Abstract

In this paper modelling time series by single hidden layer feedforward neural network models is considered. A coherent modelling strategy based on statistical inference is discussed. The problems of selecting the variables and the number of hidden units are solved by using statistical model selection criteria and tests. Misspecification tests for evaluating an estimated neural network model are considered. Forecasting with neural network models is discussed and an application to a real time series is presented.

Suggested Citation

  • Marcelo C. Medeiros & Timo Terasvirta, 2001. "Statistical methods for modelling neural networks," Textos para discussão 445, Department of Economics PUC-Rio (Brazil).
    • Handle: RePEc:rio:texdis:445
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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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