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Forecasting inflation using univariate continuous‐time stochastic models

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  • Kevin Fergusson

Abstract

In this paper we investigate the applicability of several continuous‐time stochastic models to forecasting inflation rates with horizons out to 20 years. While the models are well known, new methods of parameter estimation and forecasts are supplied, leading to rigorous testing of out‐of‐sample inflation forecasting at short and long time horizons. Using US consumer price index data we find that over longer forecasting horizons—that is, those beyond 5 years—the log‐normal index model having Ornstein–Uhlenbeck drift rate provides the best forecasts.

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  • Kevin Fergusson, 2020. "Forecasting inflation using univariate continuous‐time stochastic models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(1), pages 37-46, January.
    • Handle: RePEc:wly:jforec:v:39:y:2020:i:1:p:37-46
    DOI: 10.1002/for.2603
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