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Hypernormal densities

Author

Listed:
  • Raffaella Giacomini
  • Andreas Gottschling
  • Christian Haefke
  • Halbert White

Abstract

We propose a new family of density functions that possess both flexibility and closed form expressions for moments and anti-derivatives, making them particularly appealing for applications. We illustrate its usefulness by applying our new family to obtain density forecasts of U.S. inflation. Our methods generate forecasts that improve on standard methods based on AR-ARCH models relying on normal or Student's t-distributional assumptions.

Suggested Citation

  • Raffaella Giacomini & Andreas Gottschling & Christian Haefke & Halbert White, 2002. "Hypernormal densities," Economics Working Papers 638, Department of Economics and Business, Universitat Pompeu Fabra.
    • Handle: RePEc:upf:upfgen:638
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    References listed on IDEAS

    as
    1. James B. McDonald, 2008. "Some Generalized Functions for the Size Distribution of Income," Economic Studies in Inequality, Social Exclusion, and Well-Being, in: Duangkamon Chotikapanich (ed.), Modeling Income Distributions and Lorenz Curves, chapter 3, pages 37-55, Springer.
    2. Karim Abadir, 1999. "An introduction to hypergeometric functions for economists," Econometric Reviews, Taylor & Francis Journals, vol. 18(3), pages 287-330.
    3. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    4. 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.
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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.

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    More about this item

    Keywords

    ARMA-GARCH models; neural networks; nonparametric density estimation; forecast accuracy;
    All these keywords.

    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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