Long-memory modeling and forecasting: evidence from the U.S. historical series of inflation
Stephen Miller () and
Rangan Gupta ()
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Boubaker Heni: Rabat Business School, BEAR LAB (UIR), Technopolis Rabat-Shore, 11100Rabat-Salé, Morocco
Studies in Nonlinear Dynamics & Econometrics, 2021, vol. 25, issue 5, 289-310
We report the results of applying several long-memory models to the historical monthly U.S. inflation rate series and analyze their out-of-sample forecasting performance over different horizons. We find that the time-varying approach to estimating inflation persistence outperforms the models that assume a constant long-memory process. In addition, we examine the link between inflation persistence and exchange rate regimes. Our results support the hypothesis that floating exchange rates associate with increased inflation persistence. This finding, however, is less pronounced during the era of the Great Moderation and the Federal Reserve System’s commitment to inflation targeting.
Keywords: long memory; time-varying persistence; U.S. inflation; wavelet analysis (search for similar items in EconPapers)
JEL-codes: C13 C22 C32 C54 E31 (search for similar items in EconPapers)
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Working Paper: Long-Memory Modeling and Forecasting: Evidence from the U.S. Historical Series of Inflation (2018)
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