Macroeconomic Predictions using Payments Data and Machine Learning
Macroeconomic Predictions using Payments Data and Machine Learning
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DOI：
10.3390/forecast5040036
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
James T. E. Chapman;Ajit Desai
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文献类型：
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作者：
James T. E. Chapman;Ajit Desai
本文评估了综合支付数据对加拿大宏观经济预测的有用性。具体来说，我们评估哪种类型的支付数据是有用的，它们何时有用，为什么有用，以及机器学习（ML）模型是否增强了它们的预测价值。我们发现，使用因子模型的支付数据可以帮助提高预测GDP，零售和批发销售的准确性高达25%;非线性ML模型可以进一步提高准确性高达20%。此外，我们发现零售支付数据比批发系统的数据更有用;由于及时性，它们在危机期间和临近预报时增加了更多的价值。支付数据和ML模型的贡献在经济低增长和正常增长期间是小的和线性的。然而，在COVID-19等危机期间，它们的贡献是巨大的、不对称的和非线性的。此外，我们提出了一种交叉验证方法来减轻过度拟合，并使用工具来克服ML模型中的可解释性，以提高其政策使用的有效性。
This paper assesses the usefulness of comprehensive payments data for macroeconomic predictions in Canada. Specifically, we evaluate which type of payments data are useful, when they are useful, why they are useful, and whether machine learning (ML) models enhance their predictive value. We find payments data with a factor model can help improve accuracy up to 25% in predicting GDP, retail, and wholesale sales; and nonlinear ML models can further improve the accuracy up to 20%. Furthermore, we find the retail payments data are more useful than the data from the wholesale system; and they add more value during crisis and at the nowcasting horizon due to the timeliness. The contribution of the payments data and ML models is small and linear during low and normal economic growth periods. However, their contribution is large, asymmetrical, and nonlinear during crises such as COVID-19. Moreover, we propose a cross-validation approach to mitigate overfitting and use tools to overcome interpretability in the ML models to improve their effectiveness for policy use.