论文标题
基准计量经济学和机器学习方法。
Benchmarking Econometric and Machine Learning Methodologies in Nowcasting
论文作者
论文摘要
Nowcasting可以在为发布具有很大时间滞后的数据的及时介绍数据时发挥关键作用，例如最终的GDP数字。当前，有很多方法和方法供从业人员选择。但是，在预测性能和特征方面，缺乏对这些不同方法的全面比较。本文通过研究NOTCAST US季度GDP增长的12种不同方法的性能来解决该缺乏症，包括Nowcasting中最常用的所有方法以及一些最受欢迎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绩效：1980年代初期的经济衰退，2008年的金融危机和共同危机。分析中的两种最佳性能方法是长期短期记忆人工神经网络（LSTM）和贝叶斯矢量自动进程（BVAR）。为了促进每种检查方法的进一步应用和测试，可以与论文一起发布，其中包含可以应用于不同数据集的样板代码的开源存储库，网址为：github.com/dhopp1/nowwopp1/nowwopp1/nowcasting_benchmark。
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.
