期刊
COMPUTATIONAL ECONOMICS
卷 57, 期 1, 页码 387-417出版社
SPRINGER
DOI: 10.1007/s10614-020-10083-5
关键词
Bagging; Boosting; Dynamic Factor Model; Machine Learning; Nowcasting; Random forests
The study shows that tree-based ensemble models generally outperform linear dynamic factor models, with factors derived from real variables having a stronger influence on machine learning models. Factors derived from financial and price variables only become important in predicting GDP after the 2008-9 global financial crisis, reflecting the impact of extra loose monetary policies implemented in the period following the crisis.
In this study, we nowcast quarter-over-quarter US GDP growth rates between 2000Q2 and 2018Q4 using tree-based ensemble machine learning models, namely, bagged decision trees, random forests, and stochastic gradient tree boosting. To solve the ragged edge problem and reduce the dimension of the data set, we adopt a dynamic factor model. Dynamic factors extracted from 10 groups of financial and macroeconomic variables are fed to machine learning models for nowcasting US GDP. Our results show that tree-based ensemble models usually outperform linear dynamic factor models. Factors obtained from real variables appear to be more influential in machine learning models. The impact of factors derived from financial and price variables can only become important in predicting GDP after the great financial crisis of 2008-9, reflecting the effect extra loose monetary policies implemented in the period following the crisis.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据