在临近预报中对计量经济学和机器学习方法进行基准测试
Benchmarking Econometric and Machine Learning Methodologies in Nowcasting
摘要：
即时预报可以发挥关键作用，使决策者能够更及时地了解有很大时滞的数据，如最终国内生产总值数字。目前，有大量的方法和做法供从业人员选择。然而，缺乏这些不同的方法在预测性能和特征方面的全面比较。本文通过研究12种不同方法在预测美国季度GDP增长方面的表现来解决这一缺陷，包括预测中最常用的所有方法，以及一些最流行的传统机器学习方法。在美国经济历史上的三个不同动荡时期评估了表现：20世纪80年代初的经济衰退，2008年的金融危机和COVID危机。分析中表现最好的两种方法是长短期记忆人工神经网络（LSTM）和贝叶斯向量自回归（BVAR）。为了便于进一步应用和测试每一种研究的方法，一个包含可应用于不同数据集的样板代码的开源存储库与论文一起发布，可在github.com/dhopp1/nowcasting_benchmark上获得。
英文摘要：
Nowcasting can play a key role in giving policymakers timelier insight to data published with a significant time lag, such as final GDP figures. Currently, there are a plethora of methodologies and approaches for practitioners to choose from. However, there lacks a comprehensive comparison of these disparate approaches in terms of predictive performance and characteristics. This paper addresses that deficiency by examining the performance of 12 different methodologies in nowcasting US quarterly GDP growth, including all the methods most commonly employed in nowcasting, as well as some of the most popular traditional machine learning approaches. Performance was assessed on three different tumultuous periods in US economic history: the early 1980s recession, the 2008 financial crisis, and the COVID crisis. The two best performing methodologies in the analysis were long short-term memory artificial neural networks (LSTM) and Bayesian vector autoregression (BVAR). To facilitate further application and testing of each of the examined methodologies, an open-source repository containing boilerplate code that can be applied to different datasets is published alongside the paper, available at: github.com/dhopp1/nowcasting_benchmark.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
