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随着数智时代的来临，基于互联网、移动互联网产生的大数据与人工智能技术正在深刻重塑经济统计体系。本文在梳理传统经济统计局限性的基础上，系统探讨大数据与人工智能对经济统计的多维度影响。数据获取方式上，商品扫描数据、手机信令数据、卫星遥感数据等多源非结构化数据以及大模型生成数据正成为传统统计调查的重要补充。经济测度范式上，实现经济统计指标高频化实时化，推动基于非结构化数据的主观因素测度，借助大数据与机器学习等方法赋能基于估计与预测的经济测度，以及创新统计监督方式。统计指标体系上，从以生产为核心的传统指标向涵盖社会福利、可持续发展等多维度统计指标体系拓展。实践层面上，经济大数据的非实验性特征可能带来样本选择偏差，凸显了因果推断与循证政策评估在数智时代的挑战性。面对大数据与人工智能带来的机遇与挑战，本文提出数智时代经济统计测度方法、指标体系与理论基础等方面的若干重要发展方向，以此推动经济统计实现理论、方法与应用的协同升级。
Abstract：With the advent of the digital intelligence era, big data and artificial intelligence technologies are profoundly reshaping the economic statistical system. On the basis of reviewing the limitations of traditional economic statistics, this paper systematically explores the multi-dimensional impacts of big data and artificial intelligence on economic statistics. In terms of data acquisition methods, multi-source unstructured data such as product scanning data, mobile signaling data, satellite remote sensing data, as well as data generated by large models, are becoming important supplements to traditional statistical surveys. In terms of economic measurement paradigms, these technologies enable the high-frequency and real-time updating of economic statistical indicators, facilitate the measurement of subjective factors based on unstructured data, empower economic measurement through estimation and prediction with big data and machine learning methods, and innovate approaches to statistical oversight. Regarding the statistical indicator system, there is a gradual shift from traditional production-centered indicators toward a multi-dimensional statistical framework encompassing social welfare and sustainable development. At the practice level, the non-experimental nature of economic big data may introduce sample selection bias, highlighting the challenges of causal inference and evidence-based policy evaluation in the digital intelligence era. In response to the opportunities and challenges brought by big data and artificial intelligence, this paper proposes several important directions for the development of economic statistical measurement methods, indicator systems, and theoretical foundations in the digital intelligence era, aiming to promote the coordinated advancement of theory, methodology, and application in economic statistics.
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(1)Penn World Table最开始由美国宾夕法尼亚大学的经济学者创建，随后管理权与后续开发移交到加州大学戴维斯分校，现在由荷兰格罗宁根大学管理。
(1)The World’s Most Valuable Resource，参见https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longeroil-but-data。
(1)联合国统计大数据和数据科学全球中心：《手机信令数据在统计中的应用》，详见https://unbdc.stats.gov.cn/cxycg/rktj/202210/t20221012_991.html。
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(2)详见https://www.eurofound.europa.eu/en/surveys/european-quality-life-surveys-eqls。
(1)自下而上方法是指通过详细分析各个碳排放源（如农业、工业、自然生态系统等）的活动数据和排放因子，估算碳排放量。自下而上方法能够提供详细的碳排放源和碳汇的分布信息，适合分析特定区域或部门的排放特征，但该方法严重依赖基础数据和模型假设，可能存在不确定性。
(2)Ceres是一家成立于1989年的美国非营利倡导组织，总部位于波士顿。CookESG数据库是由Impact Cubed（前身为Arabesque）的联合创始人Robert G. Eccles和Svetlana Klimenko等人领导开发的一套商业化的企业ESG数据与评分产品。
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(2)详见https://news.cnr.cn/native/gd/20240826/t20240826_526872023.shtml。
(1)《2024-2025 Accomplishments Report:From Commitment to Continuity 》，详见https://www.statcan.gc.ca/en/about/transparency/disaggregated-data-accomplishments/disaggregated-data-accomplishments-2024-2025.pdf。
(2)[法]托马斯·皮凯蒂：《21世纪资本论》，巴曙松等译，中信出版社2014年版，第270页。
基本信息:
DOI：10.19343/j.cnki.11-1302/c.2026.03.001
中图分类号:F222;TP311.13;TP18
引用信息:
[1]洪永淼,罗良清,张明.大数据、人工智能与经济统计：机遇与挑战[J].统计研究,2026,43(03):3-24.DOI:10.19343/j.cnki.11-1302/c.2026.03.001.
基金信息:
中国科学院大学数智时代经济管理复杂系统建模教育部哲学社会科学创新团队（E5820801）; 教育部人文社会科学重点研究基地重大项目（22JJD790049）; 国家自然科学基金“计量建模与经济政策研究”基础科学中心项目（71988101）; 内蒙古自治区自然科学基金青年基金“稳健贫困脆弱性方法视角下收入流动的群体划分、影响机理与促进政策优化研究”（2025QN07015）
2026-03-25
2026-03-25