Huaqing Xie, Xingcheng Xu, Fangjia Yan, Xun Qian, Yanqing Yang
{"title":"Deep Learning for Multi-Country GDP Prediction: A Study of Model Performance and Data Impact","authors":"Huaqing Xie, Xingcheng Xu, Fangjia Yan, Xun Qian, Yanqing Yang","doi":"arxiv-2409.02551","DOIUrl":null,"url":null,"abstract":"GDP is a vital measure of a country's economic health, reflecting the total\nvalue of goods and services produced. Forecasting GDP growth is essential for\neconomic planning, as it helps governments, businesses, and investors\nanticipate trends, make informed decisions, and promote stability and growth.\nWhile most previous works focus on the prediction of the GDP growth rate for a\nsingle country or by machine learning methods, in this paper we give a\ncomprehensive study on the GDP growth forecasting in the multi-country scenario\nby deep learning algorithms. For the prediction of the GDP growth where only\nGDP growth values are used, linear regression is generally better than deep\nlearning algorithms. However, for the regression and the prediction of the GDP\ngrowth with selected economic indicators, deep learning algorithms could be\nsuperior to linear regression. We also investigate the influence of the novel\ndata -- the light intensity data on the prediction of the GDP growth, and\nnumerical experiments indicate that they do not necessarily improve the\nprediction performance. Code is provided at\nhttps://github.com/Sariel2018/Multi-Country-GDP-Prediction.git.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"9 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.02551","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.
国内生产总值是衡量一个国家经济健康状况的重要指标,反映了商品和服务生产的总价值。预测 GDP 增长对经济规划至关重要,因为它有助于政府、企业和投资者预测趋势,做出明智决策,并促进稳定和增长。虽然之前的大多数工作都集中在预测单个国家的 GDP 增长率或使用机器学习方法,但在本文中,我们对使用深度学习算法预测多国情况下的 GDP 增长进行了全面研究。对于只使用 GDP 增长值的 GDP 增长预测,线性回归通常优于深度学习算法。但是,对于带有选定经济指标的 GDP 增长的回归和预测,深度学习算法可能优于线性回归。我们还研究了新数据--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高预测性能。代码见https://github.com/Sariel2018/Multi-Country-GDP-Prediction.git。