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Boosting nonlinear predictability of macroeconomic time series

机译:促进宏观经济时间序列的非线性可预测性

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We apply the boosting estimation method in order to investigate to what extent and at what horizons macroeconomic time series have nonlinear predictability that comes from their own history. Our results indicate that the U.S. macroeconomic time series have more exploitable nonlinear predictability than previous studies have found. On average, the most favorable out-of-sample performance is obtained via a two-stage procedure, where a conventional linear prediction model is fitted first and the boosting technique is applied to build a nonlinear model for its residuals. (C) 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译:我们应用升压估计方法,以调查宏观经济时间序列具有在多大程度上和何种程度和处于自身历史上的非线性可预测性的程度和何种程度。我们的结果表明,美国宏观经济时间序列具有比以往的研究所发现的更具利用的非线性可预测性。平均而言,通过两级过程获得最有利的样本性能,其中首先拟合传统的线性预测模型,并施加升压技术以构建其残留物的非线性模型。 (c)2020国际预测研究所。由elsevier b.v出版。保留所有权利。

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