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Long-memory modeling and forecasting: evidence from the U.S. historical series of inflation
Studies in Nonlinear Dynamics & Econometrics ( IF 1.032 ) Pub Date : 2020-10-27 , DOI: 10.1515/snde-2018-0116
Heni Boubaker 1 , Giorgio Canarella 2 , Rangan Gupta 3 , Stephen M. Miller 2
Affiliation  

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.

中文翻译:

长期记忆建模和预测:来自美国历史系列通货膨胀的证据

我们报告了将半参数长记忆估计量应用于美国通货膨胀的历史月度系列的结果,并使用样本内和样本外程序分析了它们在 1、6、12 和 24 个月内的经验预测表现。出于比较目的,我们还应用了两个参数估计器,朴素的 AR(1) 和 ARFIMA(1, d, 1) 模型。我们使用均方误差 (MSE) 和平均绝对误差 (MAE) 标准评估竞争方法的预测准确性。我们使用 Diebold-Mariano (1995) 检验评估竞争预测的预测准确性的统计显着性。总体而言,我们的结果比基于 MSE 和 MAE 标准的 Lahiani 和 Scaillet (2009) 阈值估计器略好。事实证明,这种性能改进不足以导致拒绝预测准确性相等的零假设。另一方面,Boubaker (2017) 估计器在更长的范围内显着优于时不变估计器。然而,在较短的范围内,除了朴素的 AR(1) 模型外,Boubaker (2017) 估计器的预测性能并不比时不变长记忆估计器好得多。
更新日期:2020-10-27
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