.png){kind=icon}
返回
Macroeconomic data transformations matter
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijforecast.2021.05.005.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In a low-dimensional linear regression setup, considering linear transformations/combinations of predictors does not alter predictions. However, when the forecasting technology either uses shrinkage or is nonlinear, it does. This is precisely the fabric of the machine learning (ML) macroeconomic forecasting environment. Pre-processing of the data translates to an alteration of the regularization - explicit or implicit - embedded in ML algorithms. We review old transformations and propose new ones, then empirically evaluate their merits in a substantial pseudo-out-sample exercise. It is found that traditional factors should almost always be included as predictors and moving average rotations of the data can provide important gains for various forecasting targets. Also, we note that while predicting directly the average growth rate is equivalent to averaging separate horizon forecasts when using OLS-based techniques, the latter can substantially improve on the former when regularization and/or nonparametric nonlinearities are involved. (C) 2021 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
Keyword:
Machine learning
Big data
Forecasting
Feature engineering
Regularization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.1
论文数:
3.1K
被引数:
1.1W
机构
引用论文
.png){kind=icon}
分享收藏
分享收藏分享收藏
查看更多内容