Template-type: ReDIF-Paper 1.0
Author-Name: Alessandra Canepa,   
Author-Email: alessandra.canepa@unito.it 
Author-Name: Karanasos, Menelaos  
Author-Email: menelaos.karanasos@brunel.ac.uk 
Author-Name: Paraskevopoulos, Athanasios   
Author-Email: alparas@unipi.gr
Author-Name: Chini, Emilio Zanetti  
Author-Email: emilio.zanettichini@unibg.it
Author-Workplace-Name: University of Turin
Author-Workplace-Homepage: http://www.est.unito.it/
Title: Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability.
Abstract: In this paper we employ an autoregressive GARCH-in-mean-level process with variable coe˘ cients
to forecast in?ation and investigate the behavior of its persistence in the United States. We propose
new measures of time varying persistence, which not only distinguish between changes in the dynamics
of in?ation and its volatility, but are also allow for feedback between the two variables. Since it is
clear from our analysis that predictability is closely interlinked with (?rst-order) persistence we coin
the term persistapredictability. Our empirical results suggest that the proposed model has good
forecasting properties.
Length: pages 51
Creation-Date: 2022-09
File-URL: https://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2022dip/wp_12_2022.pdf
File-Format: Application/PDF
Handle: RePEc:uto:dipeco:202212