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Sparse Warcasting

delete2026-07-01
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OA
AI
M
Mihnea Constantinescu *
DOI：10.1111/sjpe.70058delete
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摘要

摘要

En 中文
在制度崩溃期间预测经济活动需要仅从替代数据源得出的即时预测。此类来源丰富但理论上无据可依且可能信息量较弱。本研究考察了在数据丰富但统计信息匮乏的背景下，稀疏监督降维是否能提取可靠信号。应用稀疏偏最小二乘法仅使用谷歌搜索类别对2022年入侵期间乌克兰GDP进行即时预测，该方法实现的即时预测误差低于无监督的主成分回归。地理细分放大了收益：首都城市搜索数据在GDP各组成部分上系统性地优于全国总量，这与生存威胁期间经济中心的信息集中化一致。
Keyword:
embedded time series
Google categories
machine learning
nowcasting
partial least squares
sparsity
AI总结

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对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

S
Scottish Journal of Political Economy
IF:
1.2
论文数:
42
被引数:
764

机构

U
university of amsterdam
学者数:
6.0W
论文数: 5.1W
被引数: 94
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