A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data

Pei Xue , Tianshun Li
{"title":"A supervised variational autoencoder framework for dimensionality reduction and predictive modeling in high-dimensional socioeconomic data","authors":"Pei Xue ,&nbsp;Tianshun Li","doi":"10.1016/j.ject.2025.06.001","DOIUrl":null,"url":null,"abstract":"<div><div>We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex nonlinear interactions through supervised latent representation learning. Empirical analyses using comprehensive cross-country data from the World Bank (196 countries, 1997–2023) demonstrate the SVAE framework’s superior predictive accuracy, interpretability, and robustness in forecasting GDP growth, highlighting its potential for policy evaluation and macroeconomic forecasting.</div></div>","PeriodicalId":100776,"journal":{"name":"Journal of Economy and Technology","volume":"4 ","pages":"Pages 9-19"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Economy and Technology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949948825000204","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

We introduce an estimation framework utilizing a Supervised Variational Autoencoder (SVAE) to address challenges posed by high-dimensional socioeconomic data. Unlike classical linear dimensionality reduction methods, such as PCA and Lasso regression, the proposed SVAE effectively captures complex nonlinear interactions through supervised latent representation learning. Empirical analyses using comprehensive cross-country data from the World Bank (196 countries, 1997–2023) demonstrate the SVAE framework’s superior predictive accuracy, interpretability, and robustness in forecasting GDP growth, highlighting its potential for policy evaluation and macroeconomic forecasting.
一个用于高维社会经济数据降维和预测建模的监督变分自编码器框架
我们引入了一个利用监督变分自编码器(SVAE)的估计框架来解决高维社会经济数据带来的挑战。与经典的线性降维方法(如PCA和Lasso回归)不同,本文提出的SVAE通过监督潜在表示学习有效地捕获复杂的非线性相互作用。利用世界银行(196个国家,1997-2023年)的综合跨国数据进行的实证分析表明,SVAE框架在预测GDP增长方面具有卓越的预测准确性、可解释性和稳健性,突出了其在政策评估和宏观经济预测方面的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信