Hypernormal Densities

Raffaella Giacomini, Christian Haefke (), Halbert White and Andreas Gottschling
Additional contact information
Raffaella Giacomini: UCLA
Andreas Gottschling: Deutsche Bank

No 2002-14, University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego

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.

Keywords: ARMA-GARCH Models; neural networks; nonparametric density estimation; forecast accuracy (search for similar items in EconPapers)
Date: 2002-09-07
Note: oai:cdlib1:
View list of references

Downloads: (external link)
http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1107&context=ucsdecon (application/pdf)

Related works:
Working Paper: Hypernormal Densities (2002)
Working Paper: Hypernormal Densities (2002)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: http://EconPapers.repec.org/RePEc:cdl:ucsdec:2002-14

Access Statistics for this paper

More papers in University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego
Contact information at EDIRC.
Series data maintained by Christopher F. Baum ().