Paper #638

Title:

Hypernormal densities

Authors:

Raffaella Giacomini, Andreas Gottschling, Christian Haefke and Halbert White

Date:

September 2002

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.

Keywords:

ARMA-GARCH models, neural networks, nonparametric density estimation, forecast accuracy

JEL codes:

C63, C53, C45

Area of Research:

Statistics, Econometrics and Quantitative Methods

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