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数据分析与知识发现  2020, Vol. 4 Issue (7): 2-17     https://doi.org/10.11925/infotech.2096-3467.2020.0325
  专题 本期目录 | 过刊浏览 | 高级检索 |
构建国家经济大脑的实践探索与初步设想 *
王建冬(),于施洋
国家信息中心大数据发展部 北京 100045
Principles on Constructing National Economic Brain
Wang Jiandong(),Yu Shiyang
Big Data Development Department, State Information Center, Beijing 100045, China
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摘要 

目的】构建国家经济大脑,运用大数据手段支撑宏观经济监测预测。【应用背景】 国家发展改革委大数据中心正在积极尝试构建形成战略、政策、项目、企业、自然人5类本体构建规则。【方法】坚持均衡与演化路径相统一,引入批判实在论分析视角,整合复杂网络、自然语言理解和时空分析等算法模型,形成宏中微观一体化分析体系。【结果】微观层面,整合打通政府和社会数据,构建动态本体库,实现以企业社会信用代码为主线统一关联全国3 000万家企业和5 000万家个体工商户的78大类、1 828个指标项;中观层面,基于复杂系统突现的三个依赖性,构建仿真分析平台;宏观层面,围绕监测经济动力(投资、消费、贸易)、产业运行和区域发展三方面,提出实践应用较成熟的15种大数据监测指数,构建传统预测与复杂性预测、行为预测和时空预测等新方法相结合的预测平台,强化风险识别。【结论】构建微观动态本体、中观仿真分析和宏观监测预测的统一框架,能够有效解决宏微观经济学脱节的理论困境,提升宏观决策科学性。

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王建冬
于施洋
关键词 大数据经济监测预测国家经济大脑    
Abstract

[Objective] This paper identifies principles to build National Economic Brain, aiming to monitor and forecast macro-economy developments with big data. [Context] The National Development and Reform Commission’s Big Data Center is trying to construct ontology construction rules based on strategies, policies, projects, enterprises, natural persons.[Methods] We integrated algorithm of complex network, natural language processing and spatio-temporal analysis to create a macro-meso-micro analysis system.[Results] At the micro level, we integrated government and social data to build a dynamic ontology library. We established a unified association based on corporate social credit codes, which includes 30 million enterprises and 50 million individual business across the country, as well as 78 categories and 1 828 indicators. At the meso level, we built a simulation analysis platform based on the three dependencies of complex systems. At the macro level, we monitored economic power (investment, consumption, and trade), along with industrial operation and regional developments. We also put forward 15 big data monitoring indicators, and then combined traditional prediction, complexity prediction, behavior prediction and space-time prediction to strengthen risk identification.[Conclusions] We constructed a framework of microscopic dynamic ontology, mesoscopic simulation analysis, and macroscopic monitoring and forecasting system. It effectively addresses the theoretical dilemma of macro/micro-economics disconnection, and promotes decision-makings for macro-economy.

Key wordsBig Data    Economic Monitoring and Forecasting    National Economic Brain
收稿日期: 2020-04-16      出版日期: 2020-07-25
ZTFLH:  TP391  
基金资助:*本文系国家发展改革委重大问题软科学研究项目“基于大数据的宏观经济监测预测应用研究”的研究成果之一
通讯作者: 王建冬     E-mail: wangjd@sic.gov.cn
引用本文:   
王建冬,于施洋. 构建国家经济大脑的实践探索与初步设想 *[J]. 数据分析与知识发现, 2020, 4(7): 2-17.
Wang Jiandong,Yu Shiyang. Principles on Constructing National Economic Brain. Data Analysis and Knowledge Discovery, 2020, 4(7): 2-17.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0325      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I7/2
研究范式 方法类型 代表性成果
均衡范式 统计分析 Müller等[23](2006); Cavallo等[24](2016); Kholodilin等[25](2009); Schneider等[26](2016);Artola等[27](2015)
演化范式 行为依赖性 复杂网络 Bustos等[28](2012); Tacchella等[29](2012); Cristelli等[30](2015); Hidalgo等[31](2009); Gao[32](2015)
观念依赖性 人工智能 Liu等[33](2014); Levenberg等[34](2013); Llorente等[35](2015)
时空依赖性 时空分析 Neffke等[36](2011); Gao[32](2015); Doll等[37](2006); Salesses等[38](2013); 杨振山等[39](2015)
Table 1  大数据经济分析的主要方法及代表性研究
Fig.1  数据资源动态本体组织管理系统框架
[1] Clarke A, Margetts H. Governments and Citizens Getting to Know Each Other? Open, Closed, and Big Data in Public Management Reform[J]. Policy & Internet, 2014,6(4):393-417.
[2] 国务院关于印发促进大数据发展行动纲要的通知[EB/OL]. [ 2020- 01- 11]. http://www.gov.cn/zhengce/content/2015-09/05/content_10137.htm.
[2] ( The State of Council of the PRC-Notice of the Development of Big Data [EB/OL]. [ 2020- 01- 11]. http://www.gov.cn/zhengce/content/2015-09/05/content_10137.htm.)
[3] 哈里·兰德雷斯, 大卫·C·柯南德尔. 经济思想史[M]. 周文译. 第4版. 北京: 人民邮电出版社, 2011: 11-12.
[3] ( Landreth H, Colander D C. History of Economic Thought [M]. Translated by Zhou Wen. The 4th Edition. Beijing: People’s Posts and Telecommunications Press, 2011: 11-12.)
[4] Kalil T. Big Data is a Big Deal [EB/OL]. [ 2012- 04- 20]. http://www.whitehouse.gov/blog/2012/03/29/big-data-big-deal.
[5] 张勇进, 王璟璇. 主要发达国家大数据政策比较研究[J]. 中国行政管理, 2014(12):113-117.
[5] ( Zhang Yongjin, Wang Jingxuan. Comparative Studies on Big Data Policy of Main Developed Countries[J]. Chinese Public Administration, 2014(12):113-117.)
[6] Turrell A, Speigner B J, Djumalieva J, et al. Transforming Naturally Occurring Text Data into Economic Statistics: The Case of Online Job Vacancy Postings [A]//Big Data for 21st Century Economic Statistics[M]. National Bureau of Economic Research, Inc., 2019.
[7] Glaeser E L, Kim H, Luca M. Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity[A]//Big Data for 21st Century Economic Statistics[M]. National Bureau of Economic Research, Inc., 2019.
[8] Guha R, Ng S. A Machine Learning Analysis of Seasonal and Cyclical Sales in Weekly Scanner Data[A]//Big Data for 21st Century Economic Statistics[M]. National Bureau of Economic Research, Inc., 2019.
[9] Aladangady A, Aron-Dine S, Dunn W, et al. From Transactions Data to Economic Statistics: Constructing Real-Time, High-Frequency, Geographic Measures of Consumer Spending[A]//Big Data for 21st Century Economic Statistics[M]. National Bureau of Economic Research, Inc., 2019.
[10] Batch A, Chen J C, Driessen A, et al. Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators[A]//Big Data for 21st Century Economic Statistics[M]. National Bureau of Economic Research, Inc., 2019.
[11] Friedman D, Konny C G, Williams B K. Big Data in the U.S. Consumer Price Index: Experiences & Plans[A]//Big Data for 21st Century Economic Statistics[M]. National Bureau of Economic Research, Inc., 2019.
[12] Bhattacharjee S, Etudo U, Cuffe J, et al. Using Public Data to Generate Industrial Classification Codes[A]//Big Data for 21st Century Economic Statistics[M]. National Bureau of Economic Research, Inc., 2019.
[13] 王建冬, 童楠楠, 易成岐. 大数据时代公共政策评估的变革:理论、方法与实践[M]. 北京: 社会科学文献出版社, 2019.
[13] ( Wang Jiandong, Tong Nannan, Yi Chengqi. The Transformation of Public Policy Assessment in the Age of Big Data: Theory, Methodology and Practice[M]. Beijing: Social Science Academic Press, 2019.)
[14] Milakovich M. Anticipatory Government: Integrating Big Data for Smaller Government[C]// Proceedings of the 2012 Internet, Policy & Politics Conferences, Oxford Internet Institute, University of Oxford. 2012.
[15] 牛文胜, 董晓博, 吴又奎. 政务信息资源目录及宏观经济大数据系统的建设及应用[J]. 价值工程, 2018,37(20):251-255.
[15] ( Niu Wensheng, Dong Xiaobo, Wu Youkui. Construction and Application of Government Information Resource Catalog and Macroeconomic Big Data System[J]. Value Engineering, 2018,37(20):251-255.)
[16] 《2018年海南省经济社会发展大数据分析报告》出炉 [EB/OL]. [ 2020- 01- 11]. http://www.hainan.gov.cn/hainan/tingju/201901/dbebcc4ad3624e4b812057a1b6712309.shtml.
[16] ( “Hainan Economic and Social Development Big Data Analysis Report 2018”Released[EB/OL]. [ 2020- 01- 11]. http://www.hainan.gov.cn/hainan/ tingju/201901/dbebcc4ad3624e4b812057a1b6712309.shtml.)
[17] 钱斌华. 税收大数据预测经济走势的宁波经验[N]. 宁波日报, 2018-03-15(011).
[17] ( Qian Binhua. Ningbo Experience of Using Tax Big Data to Predict Economic Trends[N]. Ningbo Daily, 2018-03-15(011).)
[18] 梅宏. 建设数字中国:把握信息化发展新阶段的机遇[EB/OL]. [ 2020- 01- 11]. http://www.xinhuanet.com/politics/2018-08/19/c_1123292838.htm.
[18] ( Mei Hong. Building Digital China: Seizing Opportunities in the New Stage of Informatization Development[EB/OL]. [ 2020- 01- 11]. http://www.xinhuanet.com/politics/2018-08/19/c_1123292838.htm.)
[19] 范如国. 制度演化及其复杂性[M]. 北京: 科学出版社, 2011.
[19] ( Fan Ruguo. Institutional Evolution and Complexity[M]. Beijing: Science Press, 2011.)
[20] Bhaskar R. A Realist Theory of Science[M]. London and New York: Routledge, Taylor & Francis Group, 2008: 9.
[21] Collier A. Critical Realism: An Introduction to Roy Bhaskar’s Philosophy[M]. New York: Verso, 1994.
[22] Bhaskar R. The Possibility of Naturalism: A Philosophical Critique of the Contemporary Human Sciences[M]. London and New York: Routledge, Taylor & Francis Group, 1998: 41.
[23] Müller R, Herren H M, Röthlisberger S, et al. Recent Developments in the Swiss CPI:Scanner Data,Telecommunications and Health Price Collection[C] //Proceedings of the 9th Ottawa Group Meeting on Prices. 2006.
[24] Cavallo A, Rigobon R. The Billion Prices Project: Using Online Prices for Measurement and Research[J]. Journal of Economic Perspectives, 2016,30(2):151-178.
[25] Kholodilin K A, Podstawski M, Siliverstovs B, et al. Google Searches as a Means of Improving the Nowcasts of Key Macroeconomic Variables[R/OL]. DIW Berlin Discussion Paper No.946, 2009. http://dx.doi.org/10.2139/ssrn.1507084.
[26] Schneider M J, Gupta S. Forecasting Sales of New and Existing Products Using Consumer Reviews: A Random Projections Approach[J]. International Journal of Forecasting, 2016,32(2):243-256.
doi: 10.1016/j.ijforecast.2015.08.005
[27] Artola C, Pinto F, de Pedraza García P. Can Internet Searches Forecast Tourism Inflows?[J]. International Journal of Manpower, 2015,36(1):103-116.
doi: 10.1108/IJM-12-2014-0259
[28] Bustos S, Gomez C, Hausmann R, et al. The Dynamics of Nestedness Predicts the Evolution of Industrial Ecosystems[J]. PLoS ONE, 2012,7(11):e49393.
doi: 10.1371/journal.pone.0049393 pmid: 23185326
[29] Tacchella A, Cristelli M, Caldarelli G, et al. A New Metrics for Countries’ Fitness and Products’ Complexity[J]. Scientific Reports, 2012,2:723.
doi: 10.1038/srep00723 pmid: 23056915
[30] Cristelli M, Tacchella A, Pietronero L. The Heterogeneous Dynamics of Economic Complexity[J]. PLoS ONE, 2015,10(2):e0117174.
doi: 10.1371/journal.pone.0117174 pmid: 25671312
[31] Hidalgo C A, Hausmann R. The Building Blocks of Economic Complexity[J]. Proceedings of the National Academy of Sciences of the United States of America, 2009,106(26):10570-10575.
doi: 10.1073/pnas.0900943106 pmid: 19549871
[32] ( Gao J. Quantifying Local Industry Structure of China[EB/OL]. 2015-11-18). http://gaocn.net/project. html#industry.
[33] Liu Y, Sui Z, Kang C, et al. Uncovering Patterns of Inter-Urban Trip and Spatial Interaction from Social Media Check-in Data[J]. PLoS ONE, 2014,9(1):e86026.
doi: 10.1371/journal.pone.0086026 pmid: 24465849
[34] Levenberg A, Simpson E, Roberts S, et al. Economic Prediction Using Heterogeneous Data Streams from the World Wide Web[C] //Proceedings of the ECML/PKDD 2013 Workshop on Scalable Methods in Decision Making. 2013.
[35] Llorente A, Garcia-Herranz M, Cebrian M, et al. Social Media Fingerprints of Unemployment[J]. PLoS ONE, 2015,10(5):e0128692.
doi: 10.1371/journal.pone.0128692 pmid: 26020628
[36] Neffke F, Henning M, Boschma R. How do Regions Diversify over Time? Industry Relatedness and the Development of New Growth Paths in Regions[J]. Economic Geography, 2011,87(3):237-265.
[37] Doll C N H, Muller J P, Morley J G. Mapping Regional Economic Activity from Night-Time Light Satellite Imagery[J]. Ecological Economics, 2006,57(1):75-92.
[38] Salesses P, Schechtner K, Hidalgo C A. The Collaborative Image of the City: Mapping the Inequality of Urban Perception[J]. PLoS ONE, 2013,8(7):e68400.
doi: 10.1371/journal.pone.0068400 pmid: 23894301
[39] 杨振山, 龙瀛, Nicolas Douay. 大数据对人文—经济地理学研究的促进与局限[J]. 地理科学进展, 2015,34(4):410-417.
[39] ( Yang Zhenshan, Long Ying, Nicolas Douay. Opportunities and Limitations of Big Data Applications to Human and Economic Geography: The State of the Art[J]. Progress in Geography, 2015,34(4):410-417.)
[40] 布莱恩·阿瑟. 复杂经济学:经济思想的新框架[M]. 贾拥民译. 杭州: 浙江人民出版社, 2018.
[40] ( Arthur B. Complexity and the Economy[M]. Translated by Jia Yongmin. Hangzhou: Zhejiang People’s Publishing House, 2018.)
[41] Dopfer K, Foster J, Potts J. Micro-meso-macro[J]. Journal of Evolutionary Economics, 2004,14:263-279.
[42] 维克托·迈尔·舍恩伯格, 肯尼思·库克耶. 大数据时代:生活、工作与思维的大变革[M]. 盛杨燕, 周涛译. 杭州: 浙江人民出版社, 2012: 125.
[42] ( Schönberger V M, Cukier K. Big Data: A Revolution That will Transform How We Live, Work, and Think[M]. Translated by Sheng Yangyan, Zhou Tao. Hangzhou: Zhejiang People’s Publishing House, 2012: 125.)
[43] Lawson T. Economics and Reality[M]. London: Routledge Press, 1997.
[44] Solow R M. Economic History and Economics[J]. The American Economic Review, 1985,75(2):328-331.
[45] 刘凤良, 周业安. 行为经济学:理论与扩展[M]. 北京: 中国经济出版社, 2008.
[45] ( Liu Fengliang, Zhou Yean. Behavioral Economics: Theory and Extension[M]. Beijing: China Economic Publishing House, 2008.)
[46] 易成岐, 郭鑫, 童楠楠, 等. 基于启发式社团发现模型的创新态势研判算法[J/OL]. 计算机工程与应用. http://kns.cnki.net/kcms/detail/11.2127.tp.20190626.1724.016.html.
[46] ( Yi Chengqi, Guo Xin, Tong Nannan, et al. Algorithm of Innovation Situation Analysis Based on Heuristic Model of Community Detection[J/OL]. Computer Engineering and Applications. http://kns.cnki.net/kcms/detail/11.2127.tp.20190626.1724.016.html.)
[47] 吕琳媛. 复杂网络链路预测[J]. 电子科技大学学报, 2010,39(5):651-661.
[47] ( Lv Linyuan. Link Prediction on Complex Networks[J]. Journal of University of Electronic Science and Technology of China, 2010,39(5):651-661.)
[48] 刘建国, 任卓明, 郭强, 等. 复杂网络中节点重要性排序的研究进展[J]. 物理学报, 2013,62(17):178901.
[48] ( Liu Jianguo, Ren Zhuoming, Guo Qiang, et al. Node Importance Ranking of Complex Networks[J]. Acta Physica Sinica, 2013,62(17):178901.)
[49] 易成岐, 鲍媛媛, 薛一波. 社会网络大数据分析框架及其关键技术[J]. 中兴通讯技术, 2014(1):5-10.
[49] ( Yi Chengqi, Bao Yuanyuan, Xue Yibo. Social Networks Based on Big Data: Analytical Framework and Key Techniques[J]. ZTE Technology Journal, 2014(1):5-10.)
[50] 刘峤, 李杨, 段宏, 等. 知识图谱构建技术综述[J]. 计算机研究与发展, 2016,53(3):582-600.
[50] ( Liu Qiao, Li Yang, Duan Hong, et al. Knowledge Graph Construction Techniques[J]. Journal of Computer Research and Development, 2016,53(3):582-600.)
[51] Bhaskar R. Plato etc: The Problems of Philosophy and Their Resolution[M]. London: Verso, 1994: 73.
[52] 黄燕芬, 张超. 大数据情绪指数与经济学研究: 现状、问题与展望[J]. 教学与研究, 2018(5):40-50.
[52] ( Huang Yanfen, Zhang Chao. Big Data Sentiment Index and Economic Research——Current Situation, Problems and Prospects[J]. Teaching and Research, 2018(5):40-50.)
[53] 国家发展改革委. 2017年中国居民消费发展报告[M]. 北京: 人民出版社, 2018.
[53] ( National Development and Reform Commission. 2017 Chinese Resident Consumption Development Report[M]. Beijing: People’s Publishing House, 2018.)
[54] 易成岐, 黄倩倩, 王从余, 等. 面向类不平衡问题的“职业举报人”识别方法[J]. 计算机工程与应用, 2019,55(14):1-7,23.
[54] ( Yi Chengqi, Huang Qianqian, Wang Congyu, et al. Identification Method of “Professional Whistleblower” Based on Class Imbalance Problem[J]. Computer Engineering and Applications, 2019,55(14):1-7, 23.)
[55] 李生. 自然语言处理的研究与发展[J]. 燕山大学学报, 2013,37(5):377-384.
[55] ( Li Sheng. Research and Development of Natural Language Processing[J]. Journal of Yanshan University, 2013,37(5):377-384.)
[56] 保罗·克鲁格曼. “新经济地理学”在哪里?[A] // G. L. 克拉克, M.P.费尔德曼, M.S.格特勒. 牛津经济地理学手册[M]. 刘卫东,王缉慈,李小建, 等译. 北京: 商务印书馆, 2010: 49.
[56] ( Krugman P R. Where is “New Economic Geography”[A] // Clark G L, Feldman M P, Gertler M S. Oxford Handbook of Economic Geography[M]. Translated by Liu Weidong, Wang Jici, Li Xiaojian, et al. Beijing: Commercial Press, 2010: 49.)
[57] Krugman P R. Geography and Trade[M]. Cambridge: MIT Press, 1991.
[58] 种照辉, 覃成林, 叶信岳. 城市群经济网络与经济增长——基于大数据与网络分析方法的研究[J]. 统计研究, 2018,35(1):13-21.
[58] ( Chong Zhaohui, Qin Chenglin, Ye Xinyue. Economic Networks and Economic Growth of Urban Agglomeration: An Integrated Approach of Big Data and Network Analysis[J]. Statistical Research, 2018,35(1):13-21.)
[59] 屈晓晖, 袁武, 袁文, 等. 时空大数据分析技术在传染病预测预警中的应用[J]. 中国数字医学, 2015,10(8):36-39.
[59] ( Qu Xiaohui, Yuan Wu, Yuan Wen, et al. Application of Spatio-temporal Big Data Analysis Technologies in Forecasting and Early Warning of Infectious Diseases[J]. China Digital Medicine, 2015,10(8):36-39.)
[60] 魏颖, 杨道玲, 郝凯. 基于大数据的事中事后监管创新[M]. 北京: 社会科学文献出版社, 2019.
[60] ( Wei Ying, Yang Daoling, Hao Kai. Innovation of Supervision in Process and Afterwards Based on Big Data[M]. Beijing: Social Science Academic Press, 2019.)
[61] 王建冬, 童楠楠. 数字经济背景下数据与其他生产要素的协同联动机制研究[J]. 电子政务, 2020(3):22-31.
[61] ( Wang Jiandong, Tong Nannan. Research on the Synergetic Linkage Mechanism of Data and Other Factors of Production under the Background of Digital Economy[J]. E-Government, 2020(3):22-31.)
[62] 朱庆, 付萧. 多模态时空大数据可视分析方法综述[J]. 测绘学报, 2017,46(10):1672-1677.
[62] ( Zhu Qing, Fu Xiao. The Review of Visual Analysis Methods of Multi-modal Spatio-temporal Big Data[J]. Acta Geodaetica et Cartographica Sinica, 2017,46(10):1672-1677.)
[63] 2019中国居民消费大数据指数发布[EB/OL]. [ 2020- 01- 11]. http://www.sohu.com/a/336754735_266317.
[63] ( 2019 Chinese Residents’ Consumption Big Data Index Released [EB/OL]. [ 2020- 01- 11]. http://www.sohu.com/a/336754735_266317.)
[64] 研究报告:服务消费升级增长趋势最显著[EB/OL]. [ 2020- 04- 27]. http://index.caixin.com/2017-06-29/101107130.html.
[64] ( Research Report: Service Consumption Upgrade Growth Trend is the Most Significant[EB/OL]. [ 2020- 04- 27]. http://index.caixin.com/2017-06-29/101107130.html.)
[65] “一带一路”贸易合作大数据报告[EB/OL]. [2020-01-11]. https://www.yidaiyilu.gov.cn/mydsjbg.htm.
[65] ( “Belt and Road” Trade Cooperation Big Data Report [EB/OL]. [2020-01-11]. https://www.yidaiyilu.gov.cn/mydsjbg.htm.)
[66] Elvidge C D, Sutton P C, Ghosh T, et al. A Global Poverty Map Derived from Satellite Data[J]. Computers & Geosciences, 2009,35(8):1652-1660.
[67] Sutton P, Roberts D, Elvidge C, et al. Census from Heaven: An Estimate of the Global Human Population Using Night-time Satellite Imagery[J]. International Journal of Remote Sensing, 2001,22(16):3061-3076.
[68] 易成岐, 郭鑫, 郭明军, 等. 大数据视角下企业发展瓶颈问题及对策——以S市国家高新技术企业为例[J]. 中国经贸导刊, 2019(13):54-55,77.
[68] ( Yi Chengqi, Guo Xin, Guo Mingjun, et al. Bottleneck Problems and Countermeasures for Enterprise Development from the Perspective of Big Data——Taking National High-tech Enterprises in City S as an Example[J]. China Economic & Trade Herald, 2019(13):54-55, 77.)
[69] Ohlhorst F J. Big Data Analytics: Turning Big Data into Big Money[M]. Hoboken: John Wiley & Sons, 2013.
[70] 姜疆. 基于大数据的宏观经济预测和分析[J]. 新经济导刊, 2018(9):62-66.
[70] ( Jiang Jiang. Macroeconomic Prediction and Analysis Based on Big Data[J]. New Economy Weekly, 2018(9):62-66.)
[71] 刘涛雄, 徐晓飞. 互联网搜索行为能帮助我们预测宏观经济吗?[J]. 经济研究, 2015,50(12):68-83.
[71] ( Liu Taoxiong, Xu Xiaofei. Can Internet Search Behavior Help to Forecast the Macro Economy?[J]. Economic Research Journal, 2015,50(12):68-83.)
[72] 桂春. 复杂网络中的社团发现与链路预测[D]. 兰州: 兰州大学, 2018.
[72] ( Gui Chun. Community Detection and Link Prediction of Complex Network[D]. Lanzhou: Lanzhou University, 2018.)
[73] Vosen S, Schmidt T. A Monthly Consumption Indicator for Germany Based on Internet Search Query Data[J]. Applied Economics Letters, 2012,19(7):683-687.
[74] Goel S, Hofman J M, Lahaie S, et al. Predicting Consumer Behavior with Web Search[J]. Proceedings of the National Academy of Sciences of the United States of America, 2010,107(41):17486-17490.
doi: 10.1073/pnas.1005962107 pmid: 20876140
[75] Choi H, Varian H. Predicting Initial Claims for Unemployment Benefits [EB/OL]. [2020-01-11]. https://ssrn.com/abstract=1659307.
[76] Guzmán G. Internet Search Behavior as an Economic Forecasting Tool: The Case of Inflation Expectations[J]. Journal of Economic and Social Measurement, 2011,36(3):119-167.
[77] Askitas N, Zimmermann K F. Google Econometrics and Unemployment Forecasting[J]. Discussion Papers of DIW Berlin, 2009,55(2):107-120.
[78] Ettredge M, Gerdes J, Karuga G. Using Web-based Search Data to Predict Macroeconomic Statistics[J]. Communications of the ACM, 2005,48(11):87-92.
[79] 王勇, 董恒新. 大数据背景下中国季度失业率的预测研究——基于网络搜索数据的分析[J]. 系统科学与数学, 2017,37(2):460-472.
[79] ( Wang Yong, Dong Hengxin. The Forecast of China’s Quarterly Unemployment Rate in the Background of Big Data——Analysis Based on Network Search Data[J]. Journal of Systems Science and Mathematical Sciences, 2017,37(2):460-472.)
[80] Wu L, Brynjolfsson E. The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales[A] // Goldfarb A, Greenstein S M, Tucker C E. Economic Analysis of the Digital Economy[M]. University of Chicago Press, 2015: 89-118.
[81] Kulkarni R, Haynes K E, Stough R R, et al. Forecasting Housing Prices with Google Econometrics[EB/OL]. https://ssrn.com/abstract=1438286.
[82] Dzielinski M. News Sensitivity and the Cross-Section of Stock Returns[J]. SSRN Electronic Journal.DOI: 10.2139/ssrn.1889030.
[83] Moat H S, Curme C, Stanley H E, et al. Anticipating Stock Market Movement with Google and Wikipedia[A]//Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale[M]. Springer, 2013: 47-59.
[84] Bollen J, Mao H N, Zeng X J. Twitter Mood Predicts the Stock Market[J]. Journal of Computational Science, 2011,2(1):1-8.
[85] 何龙庆, 陈伦, 蒋金勇. 基于时空数据驱动的交通流预测[J]. 交通与运输, 2019,32(S1):75-80.
[85] ( He Longqing, Chen Lun, Jiang Jinyong. Traffic Flow Prediction Based on Spatiotemporal Data[J]. Traffic & Transportation, 2019,32(S1):75-80.)
[86] 黄伟政. 基于卷积神经网络的雾霾时空演化预测方法研究[D]. 成都:电子科技大学, 2018.
[86] ( Huang Weizheng. Research on Forecast Method of Spatio-temporal Evolution of Haze Based on CNN[D]. Chengdu: University of Electronic Science of China, 2018.)
[87] Jean N, Burke M, Xie M, et al. Combining Satellite Imagery and Machine Learning to Predict Poverty[J]. Science, 2016,353(6301):790-794.
doi: 10.1126/science.aaf7894 pmid: 27540167
[88] 埃尔文·罗斯. 实验经济学概述[A] // 约翰·卡格尔, 埃尔文·罗斯.实验经济学手册[M]. 贾拥民, 陈叶烽译. 北京: 人民大学出版社, 2015: 21-22.
[88] ( Roth A. An Overview of Experimental Economics [A] //Kagel J, Roth A. The Handbook of Experimental Economics [M]. Translated by Jia Yongmin, Chen Yefeng. Beijing: Renmin University Press, 2015: 21-22.)
[89] 汪毅霖. 大数据预测与大数据时代的经济学预测[J]. 学术界, 2016(11):77-90, 325.
[89] ( Wang Yilin. Big Data Prediction and Economic Prediction in the Era of Big Data[J]. Academics, 2016 ( 11):77-90, 325.)
[90] Keynes J N. The Scope and Method of Political Economy[M]. London: Routledge, 1997.
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