Paper #638

Título:

Hypernormal densities

Autores:

Raffaella Giacomini, Andreas Gottschling, Christian Haefke y Halbert White

Data:

Septiembre 2002

Resumen:

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.

Palabras clave:

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

Códigos JEL:

C63, C53, C45

Área de investigación:

Estadística, Econometría y Métodos Cuantitativos

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