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Hypernormal Densities

Author

Listed:
  • Raffaella Giacomini

    () (Boston College)

  • Andreas Gottschling

    (Deutsche Bank)

  • Christian Haefke

    (Universitat Pompeu Fabre)

  • Halbert White

    (University of California, San Diego)

Abstract

We derive a new family of probability densities that have the property of closed-form integrability. This flexible family finds a variety of applications, of which we illustrate density forecasting from models of the AR-ARCH class for U.S. inflation. We find that the hypernormal distribution for the model's disturbances leads to better density forecasts than the ones produced under the assumption that the disturbances are Normal or Student's t.

Suggested Citation

  • Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal Densities," Boston College Working Papers in Economics 584, Boston College Department of Economics.
  • Handle: RePEc:boc:bocoec:584
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    References listed on IDEAS

    as
    1. Karim Abadir, 1999. "An introduction to hypergeometric functions for economists," Econometric Reviews, Taylor & Francis Journals, vol. 18(3), pages 287-330.
    2. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-547, August.
    3. McDonald, James B, 1984. "Some Generalized Functions for the Size Distribution of Income," Econometrica, Econometric Society, vol. 52(3), pages 647-663, May.
    4. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
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    Cited by:

    1. Alexandre Carvalho & Georgios Skoulakis, 2010. "Time Series Mixtures of Generalized t Experts: ML Estimation and an Application to Stock Return Density Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 642-687.

    More about this item

    Keywords

    ARMA-GARCH models; neural networks; nonparametric density estimation; forecast accuracy;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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