机器学习如何对宏观经济预测有用？

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摘要翻译：
我们超越了“机器学习对宏观经济预测有用吗？”通过添加“how”。目前的预测文献集中于用一种特别成功的算法来匹配特定的变量和层位。相比之下，我们研究推动ML优于标准宏观经济计量方法的基本特征的有用性。我们区分了四个所谓的特征（非线性、正则化、交叉验证和替代损失函数），并研究了它们在数据丰富和数据贫乏环境中的行为。为了做到这一点，我们设计实验，允许识别感兴趣的“治疗”效果。我们得出结论：(i)非线性是宏观经济预测真正的博弈改变者，(ii)标准因子模型仍然是最佳正则化，(iii)k倍交叉验证是最佳实践，(iv)$l2$优于$bar\epsilon$-不敏感的样本损失。非线性技术的预测收益与宏观经济高度不确定性、金融压力和房地产泡沫破裂有关。这表明，机器学习在宏观经济预测中是有用的，因为它主要捕捉了不确定性和金融摩擦背景下出现的重要非线性。
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英文标题：
《How is Machine Learning Useful for Macroeconomic Forecasting?》
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作者：
Philippe Goulet Coulombe, Maxime Leroux, Dalibor Stevanovic,
  St\'ephane Surprenant
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
--

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英文摘要：
  We move beyond "Is Machine Learning Useful for Macroeconomic Forecasting?" by adding the "how". The current forecasting literature has focused on matching specific variables and horizons with a particularly successful algorithm. In contrast, we study the usefulness of the underlying features driving ML gains over standard macroeconometric methods. We distinguish four so-called features (nonlinearities, regularization, cross-validation and alternative loss function) and study their behavior in both the data-rich and data-poor environments. To do so, we design experiments that allow to identify the "treatment" effects of interest. We conclude that (i) nonlinearity is the true game changer for macroeconomic prediction, (ii) the standard factor model remains the best regularization, (iii) K-fold cross-validation is the best practice and (iv) the $L_2$ is preferred to the $\bar \epsilon$-insensitive in-sample loss. The forecasting gains of nonlinear techniques are associated with high macroeconomic uncertainty, financial stress and housing bubble bursts. This suggests that Machine Learning is useful for macroeconomic forecasting by mostly capturing important nonlinearities that arise in the context of uncertainty and financial frictions.
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PDF链接：
https://arxiv.org/pdf/2008.12477

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关键词：宏观经济 经济预测 机器学习 econometrics epidemiology 匹配 标准 regularization 正则 features