论文标题
宏观经济预测中的可变选择，并具有许多预测因素
Variable Selection in Macroeconomic Forecasting with Many Predictors
论文作者
论文摘要
在数据丰富的环境中，使用许多经济预测因素来预测一些关键变量已成为计量经济学的新趋势。常用的方法是因子增加（FA）方法。在本文中，我们追求另一种方向，可变选择（VS）方法，以处理高维预测因子。 VS是统计和计算机科学领域的活跃主题。但是，它在经济学上的关注不如FA。本文为经济预测介绍了几种前沿与方法，其中包括：（1）经典贪婪程序； （2）L1正则化； （3）具有稀疏性的梯度下降和（4）元海拔算法。进行了全面的仿真研究，以比较其在不同情况下的可变选择准确性和预测性能。在审查的方法中，一种称为顺序蒙特卡洛算法的元海拔算法表现最好。出乎意料的是，经典的前向选择与它相当，并且比其他更复杂的算法更好。此外，我们将这些VS方法应用于经济预测，并与流行的FA方法相比。事实证明，出于就业率和CPI通货膨胀，某些VS方法可以比FA实现大幅改进，并且经济理论可以很好地解释所选的预测因子。
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.
