arXiv 2409.025512024-09-04econ.GNq-fin.EC

深度学习在多国 GDP 预测中的应用：模型性能与数据影响研究

Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact

Huaqing Xie, Xingcheng Xu, Fangjia Yan, Xun Qian, Yanqing Yang

备注：: 13 pages, 9 tables

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AI总结：

本文系统研究了深度学习在多国 GDP 增长预测中的表现，发现仅用 GDP 值时线性回归更优，而结合经济指标时深度学习可能更优，并验证光照强度数据未必能提升预测效果。

摘要：

GDP 是衡量一个国家经济健康状况的重要指标，反映了一国所生产的商品和服务的总价值。预测 GDP 增长对经济规划至关重要，因为它有助于政府、企业和投资者预判趋势、做出明智决策，并促进稳定与增长。以往大多数研究主要关注单一国家的 GDP 增长率预测或采用机器学习方法，而本文对多国场景下基于深度学习算法的 GDP 增长预测进行了全面研究。在仅使用 GDP 增长值进行 GDP 增长预测时，线性回归通常优于深度学习算法。然而，在使用选定经济指标进行 GDP 增长的回归和预测时，深度学习算法可能优于线性回归。我们还研究了新型数据——光照强度数据对 GDP 增长预测的影响，数值实验表明它们并不一定能提升预测性能。代码见 https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git。

英文摘要：

GDP is a vital measure of a country's economic health, reflecting the total value of goods and services produced. Forecasting GDP growth is essential for economic planning, as it helps governments, businesses, and investors anticipate trends, make informed decisions, and promote stability and growth. While most previous works focus on the prediction of the GDP growth rate for a single country or by machine learning methods, in this paper we give a comprehensive study on the GDP growth forecasting in the multi-country scenario by deep learning algorithms. For the prediction of the GDP growth where only GDP growth values are used, linear regression is generally better than deep learning algorithms. However, for the regression and the prediction of the GDP growth with selected economic indicators, deep learning algorithms could be superior to linear regression. We also investigate the influence of the novel data -- the light intensity data on the prediction of the GDP growth, and numerical experiments indicate that they do not necessarily improve the prediction performance. Code is provided at https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git.

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