Hypernormal Densities

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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Year of publication:	2002-09-07
Authors:	Giacomini, Raffaella ; Haefke, Christian ; White, Halbert ; Gottschling, Andreas
Institutions:	Department of Economics, University of California-San Diego (UCSD)
Subject:	ARMA-GARCH Models \| neural networks \| nonparametric density estimation \| forecast accuracy

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Extent:	application/pdf
Series:	University of California at San Diego, Economics Working Paper Series.
Type of publication:	Book / Working Paper
Notes:	The text is part of a series Department of Economics, UCSD
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010536496