Autori: Canepa, Alessandra, Zanetti Chini, Emilio, Menelaos, Karanasos, Paraskevopoulos, Athanasios
Titolo: Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability.
Periodico: Università degli studi di Torino. Dip. Di Economia e Statistica Cognetti de Martiis. Working paper series
Anno: 2022 - Volume: 4 - Fascicolo: 12 - Pagina iniziale: 1 - Pagina finale: 51

In this paper we employ an autoregressive GARCH-in-mean-level process with variable coe¢ cients to forecast inflation 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 inflation 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.




Testo completo: https://www.est.unito.it/do/home.pl/Download?doc=/allegati/wp2022dip/wp_12_2022.pdf

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