Hypernormal densities

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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Year of publication:	2002-09
Authors:	Giacomini, Raffaella ; Gottschling, Andreas ; Haefke, Christian ; White, Halbert
Institutions:	Department of Economics and Business, Universitat Pompeu Fabra
Subject:	ARMA-GARCH models \| neural networks \| nonparametric density estimation \| forecast accuracy

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Extent:	application/pdf
Series:	Economics Working Papers.
Type of publication:	Book / Working Paper
Language:	English
Classification:	C63 - Computational Techniques ; C53 - Forecasting and Other Model Applications ; C45 - Neural Networks and Related Topics
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005772145