摘要翻译：
在数据丰富的环境下，利用众多经济预测因子对少数关键变量进行预测已成为计量经济学的新趋势。常用的方法是因子增广法(FA)。在本文中，我们追求另一个方向，变量选择(VS)方法，以处理高维预测器。VS是统计学和计算机科学中的一个活跃课题。然而，在经济学中，它并不像FA那样受到重视。本文介绍了经济预测中几种前沿的VS方法，包括：（1）经典的贪婪法；(2)l1正则化；（3）稀疏化梯度下降算法和（4）元启发式算法。通过综合仿真研究，比较了它们在不同情景下的变量选择精度和预测性能。在所综述的方法中，一种称为序贯蒙特卡罗算法的元启发式算法表现最好。令人惊讶的是，经典的前向选择与它相当，并优于其他更复杂的算法。此外，我们还将这些VS方法应用于经济预测，并与流行的FA方法进行了比较。结果表明，对于就业率和CPI涨幅，一些VS方法比FA方法有较大的改进，所选择的预测因子可以用经济学理论得到很好的解释。
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英文标题：
《Variable Selection in Macroeconomic Forecasting with Many Predictors》
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作者：
Zhenzhong Wang, Zhengyuan Zhu, Cindy Yu
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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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英文摘要：
  In the data-rich environment, using many economic predictors to forecast a few key variables has become a new trend in econometrics. The commonly used approach is factor augment (FA) approach. In this paper, we pursue another direction, variable selection (VS) approach, to handle high-dimensional predictors. VS is an active topic in statistics and computer science. However, it does not receive as much attention as FA in economics. This paper introduces several cutting-edge VS methods to economic forecasting, which includes: (1) classical greedy procedures; (2) l1 regularization; (3) gradient descent with sparsification and (4) meta-heuristic algorithms. Comprehensive simulation studies are conducted to compare their variable selection accuracy and prediction performance under different scenarios. Among the reviewed methods, a meta-heuristic algorithm called sequential Monte Carlo algorithm performs the best. Surprisingly the classical forward selection is comparable to it and better than other more sophisticated algorithms. In addition, we apply these VS methods on economic forecasting and compare with the popular FA approach. It turns out for employment rate and CPI inflation, some VS methods can achieve considerable improvement over FA, and the selected predictors can be well explained by economic theories.
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PDF链接：
https://arxiv.org/pdf/2007.10160