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Hypernormal Densities

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Author Info
Raffaella Giacomini
Andreas Gottschling
Christian Haefke
Halbert White

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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.

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File URL: http://www.econ.upf.edu/docs/papers/downloads/638.pdf
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Publisher Info
Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 638.

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Date of creation: Sep 2002
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Handle: RePEc:upf:upfgen:638

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Web page: http://www.econ.upf.edu/

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Related research
Keywords: ARMA-GARCH models; neural networks; nonparametric density estimation; forecast accuracy;

Other versions of this item:

Find related papers by JEL classification:
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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References listed on IDEAS
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  1. Karim Abadir, 1999. "An introduction to hypergeometric functions for economists," Econometric Reviews, Taylor and Francis Journals, vol. 18(3), pages 287-330. [Downloadable!] (restricted)
    Other versions:
  2. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October. [Downloadable!] (restricted)
    Other versions:
  3. 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 542-47, August. [Downloadable!] (restricted)
  4. Francis X. Diebold & Anthony S. Tay & Kenneth F. Wallis, 1998. "Evaluating Density Forecasts of Inflation: The Survey of Professional Forecasters," Working Papers 98-15, New York University, Leonard N. Stern School of Business, Department of Economics.
    Other versions:
  5. A. Ron Gallant & Halbert White, 1991. "On Learning the Derivatives of an Unknown Mapping with Multilayer Feedforward Networks," University of California at San Diego, Economics Working Paper Series 89-53r, Department of Economics, UC San Diego.
  6. McDonald, James B, 1984. "Some Generalized Functions for the Size Distribution of Income," Econometrica, Econometric Society, vol. 52(3), pages 647-63, May. [Downloadable!] (restricted)
  7. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society. [Downloadable!]
  8. Chung-Ming Kuan & Halbert White, 1992. "Artificial Neural Networks: An Econometric Perspective," University of California at San Diego, Economics Working Paper Series 92-11, Department of Economics, UC San Diego.
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  9. Engle, Robert F, 1983. "Estimates of the Variance of U.S. Inflation Based upon the ARCH Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 15(3), pages 286-301, August. [Downloadable!] (restricted)
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