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Second Order Time Dependent Inflation Persistence in the United States: a GARCH-in-Mean Model with Time Varying Coefficients

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In this paper we investigate the behavior of in?ation persistence in the United States. To model in?ation we estimate an autoregressive GARCH-in-mean model with variable coe¢ cients and we propose a new measure of second-order time varying persistence, which not only distinguishes between changes in the dynamics of in?ation and its volatility, but it also allows for feedback from nominal uncertainty to in?ation. Our empirical results suggest that in?ation persistence in the United States is best described as unchanged. Another important result relates to the Monte Carlo experiment evidence which reveal that if the model is misspeci?ed, then commonly used unit root tests will misclassify in?ation of being a nonstationary, rather than a stationary process.

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  • Alessandra Canepa, & Menelaos G. Karanasos & Alexandros G. Paraskevopoulos,, 2019. "Second Order Time Dependent Inflation Persistence in the United States: a GARCH-in-Mean Model with Time Varying Coefficients," Department of Economics and Statistics Cognetti de Martiis. Working Papers 201911, University of Turin.
    • Handle: RePEc:uto:dipeco:201911
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    Cited by:

    1. 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.
    2. Canepa, Alessandra, 2022. "Ination Dynamics and Time-Varying Persistence: The Importance of the Uncertainty Channel," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202211, University of Turin.

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