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Hypernormal Densities

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Author Info
Raffaella Giacomini () (Boston College)
Andreas Gottschling (Deutsche Bank)
Christian Haefke (Universitat Pompeu Fabre)
Halbert White (University of California, San Diego)

Additional information is available for the following registered author(s):

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.

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Publisher Info
Paper provided by Boston College Department of Economics in its series Boston College Working Papers in Economics with number 584.

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Length: 42 pages
Date of creation: 01 Sep 2002
Date of revision:
Handle: RePEc:boc:bocoec:584

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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
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  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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This page was last updated on 2009-11-6.

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