EconPapers    
Economics at your fingertips  
 

Hypernormal Densities

Raffaella Giacomini (), Christian Haefke (), Halbert White and Andreas Gottschling

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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
https://www.escholarship.org/uc/item/9wr373nt.pdf;origin=repeccitec (application/pdf)

Related works:
Working Paper: Hypernormal Densities (2002) Downloads
Working Paper: Hypernormal densities (2002) Downloads
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: https://EconPapers.repec.org/RePEc:cdl:ucsdec:qt9wr373nt

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.
Bibliographic data for series maintained by Lisa Schiff ().

 
Page updated 2019-10-16
Handle: RePEc:cdl:ucsdec:qt9wr373nt