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Long-Memory Modeling and Forecasting: Evidence from the U.S. Historical Series of Inflation

Heni Boubaker, Giorgio Canarella, Rangan Gupta () and Stephen Miller ()
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Heni Boubaker: International University of Rabat, BEAR LAB, Technopolis Rabat-Shore Rocade-Sale, Morocco

No 201869, Working Papers from University of Pretoria, Department of Economics

Abstract: We report the results of applying semi-parametric long-memory estimators to the historical monthly series of U.S. inflation, and analyze their empirical forecasting performance over 1, 6, 12, and 24 months using in-sample and out-of-sample procedures. For comparison purposes, we also apply two parametric estimators, the naive AR(1) and the ARFIMA(1, d, 1) models. We evaluate the forecasting accuracy of the competing methods using the mean square error (MSE) and mean absolute error (MAE) criteria. We evaluate the statistical significance of forecasting accuracy of competing forecasts using the Diebold-Mariano (1995) test. Overall, our results preforms slightly better than the Lahiani and Scaillet (2009) threshold estimator based on the MSE and MAE criteria. This improvement in performance does not prove significant enough to cause a rejection of the null hypothesis of equality of predictive accuracy. The Boubaker (2017) estimator, on the other hand, significantly outperforms the time-invariant estimators over longer horizons. Over shorter horizons, however, the Boubaker (2017) estimator does not exhibit a significantly better predictive performance than the time-invariant long-memory estimators with the exception of the naive AR(1) model.

Keywords: long memory; wavelet analysis; time-varying persistence (search for similar items in EconPapers)
JEL-codes: C13 C22 C32 C54 E31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-for, nep-mac and nep-ore
Date: 2018-11
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