提高宏观经济时间序列的非线性可预测性
We apply the boosting estimation method in order to investigate to what extent and atwhat horizons macroeconomic time series have nonlinear predictability that comes fromtheir own history. Our results indicate that the U.S. macroeconomic time series havemore 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 techniqueis applied to build a nonlinear model for its residuals.© 2020 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
我们运用提升估计方法来研究宏观经济时间序列在多大程度上以及在什么视野下具有来自其自身历史的非线性可预测性。
我们的研究结果表明,美国宏观经济时间序列比以往研究发现的具有更多可开发的非线性可预测性。
一般来说,最有利的样本外性能是通过两阶段的程序来获得的,即首先拟合传统的线性预测模型,然后应用增强技术来建立其残差的非线性模型。
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