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Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability

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  • Alessandra Canepa,
  • Karanasos, Menelaos
  • Paraskevopoulos, Athanasios
  • Chini, Emilio Zanetti

    (University of Turin)

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.

Suggested Citation

  • Alessandra Canepa, & Karanasos, Menelaos & Paraskevopoulos, Athanasios & Chini, Emilio Zanetti, 2022. "Forecasting Ination: A GARCH-in-Mean-Level Model with Time Varying Predictability," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202212, University of Turin.
    • Handle: RePEc:uto:dipeco:202212
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