Raffaella Giacomini (),
Christian Haefke (),
Halbert White and
University of California at San Diego, Economics Working Paper Series from Department of Economics, UC San Diego
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)
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Working Paper: Hypernormal Densities (2002)
Working Paper: Hypernormal densities (2002)
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Persistent link: https://EconPapers.repec.org/RePEc:cdl:ucsdec:qt9wr373nt
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