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Hypernormal Densities

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Author Info
Raffaella Giacomini (UCLA)
Christian Haefke (Institute for Advanced Studies, Vienna)
Halbert White (University of California, San Diego)
Andreas Gottschling (Deutsche Bank)

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Abstract

We propose a new family of density function that posses 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://repositories.cdlib.org/cgi/viewcontent.cgi?article=1107&context=ucsdecon
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Publisher Info
Paper provided by Department of Economics, UC San Diego in its series University of California at San Diego, Economics Working Paper Series with number 2002-14.

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Date of creation: 07 Sep 2002
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Handle: RePEc:cdl:ucsdec:2002-14

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

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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. 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)
  2. James H. Stock & Mark W. Watson, 1999. "Forecasting Inflation," NBER Working Papers 7023, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  3. 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:
  4. 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.
  5. 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)
  6. Karim Abadir, 1999. "An introduction to hypergeometric functions for economists," Econometric Reviews, Taylor and Francis Journals, vol. 18(3), pages 287-330. [Downloadable!] (restricted)
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  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 2009-11-17.

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