Please wait a minute...
Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (1): 12-26    DOI: 10.11925/infotech.2096-3467.2019.1380
Current Issue | Archive | Adv Search |
Monitoring and Forecasting Economic Performance with Big Data
Jiandong Wang()
Big Data Development Department, State Information Center, Beijing 100045,China
Download: PDF (817 KB)   HTML ( 49
Export: BibTeX | EndNote (RIS)      

[Objective] This article reviews the current research in economic monitoring with big data from China and abroad.[Coverage] We searched the WOS, CNKI, and EI databases with the keywords of “Big data + Economics / Economy”. A total of 163 Chinese papers and 107 English papers, as well as seven monographs on the big data economics were retrieved. 157 representative documents were identified based on their relevance and quality.[Methods] This paper summarized the research methods, data sources, and conclusions of the retrieved literature published in the past ten years.[Results] Seven typical research paths were found from the perspectives of monitoring and forecasting. The former includes improving traditional surveys with big data, constructing new economic monitoring indicators, “nowcasting”, and analyzing economic performance. The latter includes building advance economic forecasting indicators, improving traditional forecasting models, and establishing new forecasting models.[Limitations] This article only examines the related research in the past ten years from specific fields, which needs to be further expanded.[Conclusions] Using big data for macroeconomic monitoring and forecasting has huge potentiality and practical dilemma. The differences and connections between big data and traditional economic analytics, as well as their impacts, also merit study.

Key wordsBig Data      Economic Monitoring      Economic Forecasting      Nowcasting     
Received: 30 December 2019      Published: 14 March 2020
ZTFLH:  TP391  
Corresponding Authors: Jiandong Wang     E-mail:

Cite this article:

Jiandong Wang. Monitoring and Forecasting Economic Performance with Big Data. Data Analysis and Knowledge Discovery, 2020, 4(1): 12-26.

URL:     OR

[1] Fayyad U M, Uthurusamy R. KDD- 95: First International Conference on Knowledge Discovery and Data Mining[C]// Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD95), Montreal, Canada. AAAI Press, 1995.
[2] 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: 2-3.
[2] ( 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: 2-3.)
[3] 李华杰, 史丹, 马丽梅 . 经济学家[J].经济学家,2018(6):96-104.
[3] ( Li Huajie, Shi Dan, Ma Limei . Economic Research Based on Big Data Methods: Frontier Progress and Research Review[J]. Economist, 2018(6):96-104.)
[4] 俞立平 . 中国软科学[J].中国软科学,2013(7):177-183.
[4] ( Yu Liping . Big Data and Big Data Economics[J]. China Soft Science, 2013(7):177-183.)
[5] 钟穗 . 信息系统工程[J].信息系统工程,2018(3):156.
[5] ( Zhong Sui . Research on the Development of General Equilibrium Economics Theory by Big Data Economics[J]. China CIO News, 2018(3):156.)
[6] 徐晋 . 大数据经济学[M]. 上海: 上海交通大学出版社, 2014: 11-12,96.
[6] ( Xu Jin. Big Data Economics[M]. Shanghai: Shanghai Jiao Tong University Press, 2014: 11-12,96.)
[7] 尹伯成, 赵红军 . 西方经济学说史:从市场经济视角的考察[M]. 第3版. 上海: 复旦大学出版社, 2017: 295.
[7] ( Yin Bocheng, Zhao Hongjun. History of Western Economics: From the Perspective of Market Economy [M]. The 3rd Edition. Shanghai: Fudan University Press, 2017: 295.)
[8] 宋圭武 . 发展[J].发展,2014(7):5.
[8] ( Song Guiwu . Economics in the Big Data Era[J]. Developing, 2014(7):5.)
[9] 俞立平 . 大数据经济学的概念、框架与学科定位研究[J]. 统计与信息论坛, 2015,30(6):3-7.
[9] ( Yu Liping . The Study on Sources, Framework and Subject Positioning of Big Data Economics[J]. Statistics & Information Forum, 2015,30(6):3-7.)
[10] 全国哲学社会科学工作办公室. 基于大数据的宏观经济现时预测理论与方法研究中期检查报告[EB/OL]. [ 2020- 01- 11]. .
[10] ( National Office for Philosophy and Social Sciences. Mid-term Inspection Report on Macroeconomic Now-casting Theories and Methods Based on Big Data [EB/OL]. [ 2020- 01- 11]. .)
[11] 刘涛雄, 徐晓飞 . 国外理论动态[J].国外理论动态,2015(1):57-64.
[11] ( Liu Taoxiong, Xu Xiaofei . Review of Big Data and Macroeconomic Analysis[J]. Foreign Theoretical Trends, 2015(1):57-64.)
[12] 徐晓飞 . 大数据视角下宏观经济预测的技术与方法研究[M]. 北京: 中国财政经济出版社, 2017: 52-54.
[12] ( Xu Xiaofei. Research on Technology and Method of Macroeconomic Forecasting from the Perspective of Big Data [M]. Beijing: China Financial and Economic Publishing House, 2017: 52-54.)
[13] Ohlhorst F J . Big Data Analytics: Turning Big Data Into Big Money[M]. Hoboken: John Wiley & Sons, 2013.
[14] Welcome to Econinformatics: Home of Economics & Big Data[EB/OL]. [ 2020- 01- 11]. .
[15] Di Francesco R . Big Data Economics, Towards Data Market Places: Nature of Data, Exchange Mechanisms, Prices, Choices, Agents & Ecosystems[M]. Amazon, 2014.
[16] Barabasi A L . Bursts: The Hidden Patterns Behind Everything We Do, from Your E-mail to Bloody Crusades[M]. Boston, MA: Dutton Books, 2010.
[17] Ginsberg J, Mohebbi M H, Patel R S , et al. Detecting Influenza Epidemics Using Search Engine Query Data[J]. Nature, 2009,457(7232):1012-1014.
[18] Choi H, Varian H R. Predicting Initial Claims for Unemployment Benefits[J].Social Science Electronic Publishing, 2010..
[19] Choi H, Varian H R . Predicting the Present with Google Trends[J]. Economic Record, 2012,88:2-9.
[20] Susan Athey [EB/OL]. [2020-01-11]..
[21] 许伟 . 基于网络大数据的社会经济监测预警研究[M]. 北京: 科学出版社, 2016: 31- 35, 98-108.
[21] ( Xu Wei. Research on Social and Economic Monitoring and Early Warning Based on Network Big Data[M]. Beijing: Science Press, 2016: 31- 35, 98-108.)
[22] Butler D . When Google Got Flu Wrong: US Outbreak Foxes a Leading Web-Based Method for Tracking Seasonal Flu[J]. Nature, 2013,494(7436):155-157.
[23] Einav L, Levin J . Economics in the Age of Big Data[J]. Science, 2014,346(6210):1243089.
[24] Balasubramanian N, Sivadasan J . What Happens When Firms Patent? New Evidence from US Economic Census Data[J]. The Review of Economics and Statistics, 2011,93(1):126-146.
[25] 马建堂 . 大数据在政府统计中的探索与应用[M]. 北京: 中国统计出版社, 2013.
[25] ( Ma Jiantang. Exploration and Application of Big Data in Government Statistics[M]. Beijing: China Statistics Press, 2013.)
[26] 徐先华 . 时代金融[J].时代金融,2018(11):49-50.
[26] ( Xu Xianhua . Analysis and Exploration of Economic Census Data in the Big Data Era[J]. Times Finance, 2018(11):49-50.)
[27] 杜成琳 .大数据时代我国经济统计现状及趋势研究[J].知识经济, 2018( 14): 14, 16.
[27] ( Du Chenglin . Research on the Status and Trend of China’s Economic Statistics in the Big Data Era[J]. Knowledge Economy, 2018( 14): 14, 16.)
[28] 张维扬 . 现代经济信息[J].现代经济信息,2018(1):104.
[28] ( Zhang Weiyang . Research on the Status and Trend of China’s Economic Statistics in the Big Data Era[J]. Modern Economic Information, 2018(1):104.)
[29] 李勇, 罗良清, 张敏 , 等. 大数据时代我国经济统计现状及趋势研究[J].中国统计, 2016(12):21-23.
[29] ( Li Yong, Luo Liangqing, Zhang Min , et al. Research on the Status and Trend of China’s Economic Statistics in the Big Data Era[J]. China Statistics, 2016(12):21-23.)
[30] 吴毅 . 经贸实践[J].经贸实践,2017(24):71.
[30] ( Wu Yi . Analysis and Improvement Measures of Urban Economic Statistics Under Big Data Environment[J]. Economic & Trade, 2017(24):71.)
[31] 王怡博, 方颀卓 . 中国市场[J].中国市场,2018(3):189-190.
[31] ( Wang Yibo, Fang Qizhuo . On the Application of Cloud Data Analysis in Economic Statistics[J]. China Market, 2018(3):189-190.)
[32] 祝旭 . 经济统计中大数据分析的应用研究[J]. 无线互联科技, 2018,15(17):110-111.
[32] ( Zhu Xu . Study on the Application of Big Data Analysis in Economic Statistics[J]. Wireless Internet Technology, 2018,15(17):110-111.)
[33] 何海 . 大数据在区域经济统计中的应用研究[J].现代营销:下旬刊, 2018(1):102.
[33] ( He Hai . Research on the Application of Big Data in Regional Economic Statistics[J]. Marketing Management Review, 2018(1):102.)
[34] 解路英 . 大数据使“定制”新的经济指标成为可能[N]. 经济参考报, 2014 -07-11(005).
[34] ( Xie Luying . Big Data Makes It Possible to “Customize” New Economic Indicators[N]. Economic Information Daily, 2014 -07-11(005).)
[35] 李世伟 . 市场经济与价格[J].市场经济与价格,2015(7):10-14.
[35] ( Li Shiwei . Improvement of National Economic Accounting System in Big Data Environment[J].Market Economy & Price, 2015(7):10-14.)
[36] 李红艳, 汪涛 . 大数据时代背景下的新型国民经济核算体系研究[J].经济视角(上), 2013(8):24-26.
[36] ( Li Hongyan, Wang Tao . Research on New National Accounting System in the Big Data Era[J]. Economic Vision, 2013(8):24-26.)
[37] How China’s Next Prime Minister Keeps Tabs on Its Economy[J]. The Economist, 2010,397:54.
[38] 新浪财经. 三一重工推出挖掘机指数一定程度反映经济走势[EB/OL]. [ 2020- 01- 11]. .
[38] ( Sina Finance. SANY Heavy Industry Launches Excavator Index, Which can Reflect Economic Trends to Some Extent[EB/OL]. [ 2020- 01- 11]. .)
[39] 韩胜娟, 张敏 . 价格理论与实践[J].价格理论与实践,2017(4):84-87.
[39] ( Han Shengjuan, Zhang Min . Fusion of the Official Price Index and the Unofficial Price Index Under the Big Data Era——Based on Comparative Study of aSPI and CPI,RPI[J]. Price: Theory & Practice, 2017(4):84-87.)
[40] 陈沁, 沈明高, 沈艳 .财智BBD中国新经济指数技术报告[EB/OL]. ( 2016- 03- 04). .
[40] ( Chen Qin, Chen Minggao, Shen Yan .BBD Think Tank: New Economy Index of China[EB/OL]. ( 2016- 03- 04). .)
[41] 国家信息中心发布《中国大数据发展报告(2017)》[EB/OL]. [ 2020- 01- 11]. .
[41] ( State Information Center Releases China Big Data Development Report (2017)[EB/OL]. [ 2020- 01- 11]. .)
[42] 国家信息中心发布《中国共享经济发展年度报告(2019)》[EB/OL]. [ 2020- 01- 11]. .
[42] ( State Information Center Releases Annual Report on China’s Sharing Economy Development (2019)[EB/OL]. [ 2020- 01- 11]. .)
[43] 张秋雁, 宋强 . 基于用电大数据的经济分析平台设计研究[J]. 电力大数据, 2017,20(8):6-9.
[43] ( Zhang Qiuyan, Song Qiang . Design and Research of Economic Analysis Platform Based on Power Consumption of Big Data Mining[J]. Power Systems and Big Data, 2017,20(8):6-9.)
[44] 王凯军, 龙厚印, 吴良良 , 等. 经济研究导刊[J].经济研究导刊,2017(25):38-39.
[44] ( Wang Kaijun, Long Houyin, Wu Liangliang , et al. Research on Industrial Structure Adjustment and Economic Indicators Based on Electric Power Big Data[J]. Economic Research Guide, 2017(25):38-39.)
[45] 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.
[46] Elvidge C D, Baugh K E, Kihn E A , et al. Relation Between Satellite Observed Visible-Near Infrared Emissions, Population, Economic Activity and Electric Power Consumption[J]. International Journal of Remote Sensing, 1997,18(6):1373-1379.
[47] Šćepanović S, Mishkovski I, Hui P , et al. Mobile Phone Call Data as a Regional Socio-Economic Proxy Indicator[J]. PLoS ONE, 2015,10(4):e0124160.
[48] Eagle N, Macy M, Claxton R . Network Diversity and Economic Development[J]. Science, 2010,328(5981):1029-1031.
[49] Holzbauer B O, Szymanski B K, Nguyen T , et al. Social Ties as Predictors of Economic Development[C]// Proceedings of the 12th International Conference and School on Network Science. Springer International Publishing 2016: 178-185.
[50] Liu J, Wang J, Shao J , et al. Online Social Activity Reflects Economic Status[J]. Physica A: Statistical Mechanics and Its Applications, 2016,457:581-589.
[51] 曲延玲 . 金融电子化[J].金融电子化,2017(5):40-41.
[51] ( Qu Yanling . Building an In-depth Economic and Financial Monitoring and Analysis System Based on Big Data[J]. Financial Computerizing, 2017(5):40-41.)
[52] Della-Penna N, Huang H F . Constructing Consumer Sentiment Index for U.S. Using Google Searches[R]. Working Papers 2009-26, University of Alberta, Department of Economics.
[53] Suhoy T . Monthly Assessments of Private Consumption[OL]. [2020-01-11]..
[54] 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.
[55] 李映桥 . 网络消费者信心与宏观经济波动关系研究[D]. 长春:吉林大学, 2017.
[55] ( Li Yingqiao . A Study on Correlation Between Web-query-based-CCI and Marco Economics[D]. Changchun: Jilin University, 2017.)
[56] 孙毅, 吕本富, 陈航 , 等. 基于网络搜索行为的消费者信心指数构建及应用研究[J]. 管理评论, 2014,26(10):117-125.
[56] ( Sun Yi, Lv Benfu, Chen Hang , et al. Research on the Establishment and Application of the Consumer Confidence Index Based on Network Search Behavior[J]. Management Review, 2014,26(10):117-125.)
[57] 刘伟江, 李映桥 . 网络消费者信心指数和经济增长的动态相关性研究[J]. 财贸研究, 2017,28(5):1-10.
[57] ( Liu Weijiang, Li Yingqiao . Dynamic Correlation Between Web Consumer Confidence Index and Economic Growth[J]. Finance and Trade Research, 2017,28(5):1-10.)
[58] 孙毅, 吕本富, 陈航 , 等. 管理世界[J].管理世界,2014(4):171-172.
[58] ( Sun Yi, Lv Benfu, Chen Hang , et al. Research on the Measurement and Application of Inflation Expectation from the Perspective of Big Data[J].Management World, 2014(4):171-172.)
[59] 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.
[60] 袁铭 . 基于网购搜索量的CPI及时预测模型[J]. 统计与信息论坛, 2015,30(4):20-27.
[60] ( Yuan Ming . A Nowcast Model of CPI Based on Search Volume of Online Shopping[J]. Statistics & Information Forum, 2015,30(4):20-27.)
[61] 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.
[62] 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.
[63] 徐映梅, 高一铭 . 基于互联网大数据的CPI舆情指数构建与应用——以百度指数为例[J]. 数量经济技术经济研究, 2017,34(1):94-112.
[63] ( Xu Yingmei, Gao Yiming . Construction of the Public Opinion Index of CPI Based on the Internet Big Data[J]. The Journal of Quantitative & Technical Economics, 2017,34(1):94-112.)
[64] 张崇, 吕本富, 彭赓 , 等. 网络搜索数据与CPI的相关性研究[J]. 管理科学学报, 2012,15(7):50-59,70.
[64] ( Zhang Chong, Lv Benfu, Peng Geng , et al. A Study on Correlation Between Web Search Data and CPI[J]. Journal of Management Sciences in China, 2012,15(7):50-59,70.)
[65] 明文彪 . 大数据背景下宏观经济分析及对浙江的建议[J]. 浙江树人大学学报:人文社会科学, 2017,17(3):54-59.
[65] ( Ming Wenbiao . Macro Economy Analysis in the Background of Big Data and Suggestions for Zhejiang Province[J]. Journal of Zhejiang Shuren University: Humanities and Social Sciences, 2017,17(3):54-59.)
[66] 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.
[67] Askitas N, Zimmermann K F . Google Econometrics and Unemployment Forecasting[J]. Applied Economics Quarterly, 2009,55(2):107-120.
[68] Francesco D A . Predicting Unemployment in Short Samples with Internet Job Search Query Data[R]. MPRA Paper 18403, 2009, University Library of Munich,Germany.
[69] McLaren N, Shanbhogue R . Using Internet Search Data as Economic Indicators[J]. Bank of England Quarterly Bulletin, 2011,51(2):134-140.
[70] Barreira N, Godinho P, Melo P . Nowcasting Unemployment Rate and New Car Sales in South-Western Europe with Google Trends[J]. NETNOMICS: Economic Research and Electronic Networking, 2013,14(3):129-165.
[71] Pavlicek J, Kristoufek L . Can Google Searches Help Nowcast and Forecast Unemployment Rates in the Visegrad Group Countries?[OL]. arXiv Preprint, arXiv: 1408. 6639.
[72] Su Z . Chinese Online Unemployment-Related Searches and Macroeconomic Indicators[J]. Frontiers of Economics in China, 2014,9(4):573-605.
[73] Pavlicek J, Kristoufek L . Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries[J]. PLoS ONE, 2015,10(5):e0127084.
[74] 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.
[74] ( 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.)
[75] Toole J L, Lin Y-R, Muehlegger E , et al. Tracking Employment Shocks Using Mobile Phone Data[J]. Journal of the Royal Society Interface, 2015,12(107).
[76] Llorente A, Garcia-Herranz M, Cebrian M , et al. Social Media Fingerprints of Unemployment[J]. PLoS ONE, 2015,10(5):e0128692.
[77] Pappalardo L, Vanhoof M, Gabrielli L . Estimating Economic Development with Mobile Phone Data[EB/OL]. [ 2020- 01- 11]. .
[78] 王智, 余桂南, 赵路 . 基于大数据的我国新经济服务业人才需求景气指数研究[ C]// 2016年全国统计建模大赛, 2016.
[78] ( Wang Zhi, Yu Guinan, Zhao Lu . Research on China’s New Economy Service Industry Talent Demand Prosperity Index Based on Big Data[ C]//Proceedings of 2016 National Statistical Modeling Competition. 2016.)
[79] Webb G K . Internet Search Statistics as a Source of Business Intelligence:Searches on Foreclosure as an Estimate of Actual Home Foreclosures[J]. Issues in Information Systems, 2009,10(2):82-87.
[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[J]. SSRN Electronic journal, 2009. .
[82] Widgrén J . Predicting Housing Prices with Google Searches in Finland[OL]. [ 2020- 01- 11]. .
[83] 董倩, 孙娜娜, 李伟 . 基于网络搜索数据的房地产价格预测[J]. 统计研究, 2014,31(10):81-88.
[83] ( Dong Qian, Sun Na’na, Li Wei . Real Estate Price Prediction Based on Web Search Data[J]. Statistical Research, 2014,31(10):81-88.)
[84] 洪涛, 厉伟 . 基于网络搜索数据的住房价格预期与实际价格波动分析[J]. 统计与信息论坛, 2015,30(11):49-53.
[84] ( Hong Tao, Li Wei . A Study on the Relationship Between Expectation and Real Housing Prices Based on Query Index[J]. Statistics & Information Forum, 2015,30(11):49-53.)
[85] 霍琳, 尚维, 徐山鹰 . 房地产开源舆情指数构建与政策影响研究[J].信息系统学报, 2013(2):57-66.
[85] ( Huo Lin, Shang Wei, Xu Shanying . Research on Public Opinion on Real Estate and the Policy Influence[J]. China Journal of Information Systems, 2013(2):57-66.)
[86] 王钧超 . 大数据时代产业经济信息分析及在宏观决策中的应用——以钢铁行业为例[D]. 北京: 中国地质大学, 2016.
[86] ( Wang Junchao . Analysis of Industrial Economic Information and Its Application in Macro Decision in the Era of Big Data——Taking Steel Industry as an Example[D]. Beijing: China University of Geosciences, 2016.)
[87] Li Y, Arora S, Youtie J , et al. Using Web Mining to Explore Triple Helix Influences on Growth in Small and Mid-Size Firms[J]. Technovation, 2018,76:3-14.
[88] Daepp M I G, Hamilton M J, West G B , et al. The Mortality of Companies[J]. Journal of the Royal Society Interface, 2015,12(106):20150120.
[89] 毛红卫, 阮伯虎, 吴铭宇 , 等. 民营科技[J].民营科技,2018(2):182-185.
[89] ( Mao Hongwei, Ruan Bohu, Wu Mingyu , et al. Monitoring and Research on the Economic Operation of Big Data in Zhuji City[J].Min Ying Ke Ji, 2018(2):182-185.)
[90] 王展 . 大数据经济运行系统的研究[D]. 郑州: 郑州大学, 2017.
[90] ( Wang Zhan . Research of Economic Operation System Based on Big Data[D]. Zhengzhou: Zhengzhou University, 2017.)
[91] 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.
[92] Lin J Y . New Structural Economics: A Framework for Rethinking Development and Policy[M]. US: World Bank Publications, 2012.
[93] 高见, 周涛 . 大数据揭示经济发展状况[J]. 电子科技大学学报, 2016,45(4):625-633.
[93] ( Gao Jian, Zhou Tao . Big Data Reveal the Status of Economic Development[J]. Journal of University of Electronic Science and Technology of China, 2016,45(4):625-633.)
[94] Hartmann D, Guevara M R, Jara-Figueroa C , et al. Linking Economic Complexity, Institutions, and Income Inequality[J]. World Development, 2017,93:75-93.
[95] 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.
[96] Hidalgo C A, Castañer E E . Do We Need Another Coffee House? The Amenity Space and the Evolution of Neighborhoods[OL]. arXiv Preprint, arXiv: 1509. 02868.
[97] Gao J . Quantifying Local Industry Structure of China[EB/OL]. ( 2015- 11- 18). .
[98] Louail T, Lenormand M, Arias J M , et al. Crowdsourcing the Robin Hood Effect in Cities[J]. Applied Network Science, 2017,2:11-13.
[99] Youn H, Bettencourt L M A, Lobo J , et al. Scaling and Universality in Urban Economic Diversification[J]. Journal of the Royal Society Interface, 2016,13(114):20150937.
[100] 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.
[100] ( 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.)
[101] Nelson A C, Duncan J B. Growth Management Principles and Practices[M]. Chicago, IL: Planners Press, 1995.
[102] Tan K C, Lim H S, Matjafri M Z , et al. Landsat Data to Evaluate Urban Expansion and Determine Land Use/Land Cover Changes in Penang Island, Malaysia[J]. Environmental Earth Sciences, 2010,60(7):1509-1521.
[103] Long Y, Liu X . How Mixed is Beijing, China: A Visual Exploration of Mixed Land Use[J]. Environment and Planning A, 2013,45(12):2797-2798.
[104] 秦萧, 甄峰, 熊丽芳 , 等. 大数据时代城市时空间行为研究方法[J]. 地理科学进展, 2013,32(9):1352-1361.
[104] ( Qin Xiao, Zhen Feng, Xiong Lifang , et al. Methods in Urban Temporal and Spatial Behavior Research in the Big Data Era[J]. Progress in Geography, 2013,32(9):1352-1361.)
[105] Long Y, Han H, Tu Y , et al. Evaluating the Effectiveness of Urban Growth Boundaries Using Human Mobility and Activity Records[J]. Cities, 2015,46:76-84.
[106] 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.
[106] ( 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.)
[107] 赵渺希, 吴康, 刘行健 , 等. 城市网络的一种算法及其实证比较[J]. 地理学报, 2014,69(2):169-183.
[107] ( Zhao Miaoxi, Wu Kang, Liu Xingjian , et al. A Novel Method to Approximate Intercity Networks and Its Empirical Validation[J]. Acta Geographica Sinica, 2014,69(2):169-183.)
[108] 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.
[109] 刘正兵, 刘静玉, 何孝沛 , 等. 中原经济区城市空间联系及其网络格局分析——基于城际客运流[J]. 经济地理, 2014,34(7):58-66.
[109] ( Liu Zhengbing, Liu Jingyu, He Xiaopei , et al. The Spatial Connection and Network Feature of Zhongyuan Economic District Base on Intercity Traffic Flow[J]. Economic Geography, 2014,34(7):58-66.)
[110] 侯赟慧, 刘志彪, 岳中刚 . 长三角区域经济一体化进程的社会网络分析[J].中国软科学, 2009(12):90-101.
[110] ( Hou Yunhui, Liu Zhibiao, Yue Zhonggang . Social Network Analysis over the Process of Economic Integration in the Yangtze River Delta[J]. China Soft Science, 2009(12):90-101.)
[111] 陈彦光, 刘继生 . 基于引力模型的城市空间互相关和功率谱分析——引力模型的理论证明、函数推广及应用实例[J]. 地理研究, 2002,21(6):742-751.
[111] ( Chen Yanguang, Liu Jisheng . Derivation and Generalization of the Urban Gravitational Model Using Fractal Idea with an Application to the Spatial Cross-Correlation Between Beijing and Tianjin[J]. Geographical Research, 2002,21(6):742-751.)
[112] 李涛, 周锐, 苏海龙 , 等. 长三角区域经济一体化水平的测度:以关系型大数据为基础[ C]// 2015中国城市规划年会. 2015.
[112] ( Li Tao, Zhou Rui, Su Hailong , et al. Measurement of Yangtze River Delta Regional Economic Integration Level: Based on Relational Big Data[ C]// Proceedings of the 2015 Annual National Planning Conference. 2015.)
[113] Hidalgo C A, Klinger B, Barabási A L , et al. The Product Space Conditions the Development of Nations[J]. Science, 2007,317(5837):482-487.
[114] 何大安, 杨益均 . 大数据时代政府宏观调控的思维模式[J]. 学术月刊, 2018,50(5):68-77.
[114] ( He Daan, Yang Yijun . Research on the Thinking Mode of Government Macro Regulation in Big Data Era[J]. Academic Monthly, 2018,50(5):68-77.)
[115] 罗慧如 . 大数据时代的宏观经济调控及政策优化[D]. 广州:广东财经大学, 2016.
[115] ( Luo Huiru . Macroeconomic Regulation and Policy Optimization in the Era of Big Data[D]. Guangzhou: Guangdong University of Finance & Economics, 2016.)
[116] 弗里德里希·冯·哈耶克 . 哈耶克文选:哈耶克论文演讲集[M]. 冯克利译. 南京: 江苏人民出版社, 2000: 450-452.
[116] ( von Hayek F . Anthology of Hayek: Hayek’s Essays and Lectures[M]. Translated by Feng Keli. Nanjing: Jiangsu People’s Publishing House, 2000: 450-452.)
[117] 蔡跃洲 . 大数据改变经济预测范式[N]. 中国社会科学报, 2015 -12-09(004).
[117] ( Cai Yuezhou . Big Data Changes the Paradigm of Economic Forecasting[N]. Chinese Social Sciences Today, 2015 -12-09(004).)
[118] 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.
[118] ( Huang Yanfen, Zhang Chao . Big Data Sentiment Index and Economic Research——Current Situation, Problems and Prospects[J]. Teaching and Research, 2018(5):40-50.)
[119] Liu Y, Huang X, An A, et al. ARSA: A Sentiment-Aware Model for Predicting Sales Performance Using Blogs [C]//Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 2007: 607-614.
[120] 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.
[121] Khadivi P, Ramakrishnan N. Wikipedia in the Tourism Industry: Forecasting Demand and Modeling Usage Behavior [C]//Proceedings of the 28th IAAI Conference. 2016.
[122] Bagnoli M, Beneish M D, Watts S G . Whisper Forecasts of Quarterly Earnings Per Share[J]. Journal of Accounting and Economics, 1999,28(1):27-50.
[123] Tumarkin R, Whitelaw R F . News or Noise? Internet Postings and Stock Prices[J]. Financial Analysts Journal, 2001,57(3):41-51.
[124] 林振兴 . 网络讨论、投资者情绪与IPO抑价[J]. 山西财经大学学报, 2011,33(2):23-29.
[124] ( Lin Zhenxing . Internet Discussions, Investor Sentiment and IPO Underpricing[J]. Journal of Shanxi Finance and Economics University, 2011,33(2):23-29.)
[125] Bollen J, Mao H, Zeng X . Twitter Mood Predicts the Stock Market[J]. Journal of Computational Science, 2011,2(1):1-8.
[126] Oh C, Sheng O R L . Investigating Predictive Power of Stock Micro Blog Sentiment in Forecasting Future Stock Price Directional Movement [C]//Proceedings of the 32nd International Conference on Information Systems. 2011.
[127] O’Connor B, Balasubramanyan R, Routledge B R, et al. From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series [C]// Proceedings of the 4th International AAAI Conference on Weblogs and Social Media. 2010.
[128] Zhou S, Shi X, Sun Y , et al. Stock Market Prediction Using Heat of Related Keywords on Micro Blog[J]. Journal of Software Engineering and Applications, 2013,6:37-41.
[129] Das S R, Chen M Y . Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web[J]. Management Science, 2007,53(9):1375-1388.
[130] Moat H S, Curme C, Stanley H E , et al. Anticipating Stock Market Movement with Google and Wikipedia[A]//Matrasulov D, Stanley H E. Nonlinear Phenomena in Complex Systems: From Nano to Macro Scale[M]. 2014: 47-59.
[131] Askitas N, Zimmermann K F . Nowcasting Business Cycles Using Toll Data[J]. Journal of Forecasting, 2013,32(4):299-306.
[132] 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).
[132] ( Qian Binhua . Ningbo Experience of Using Tax Big Data to Predict Economic Trends[N]. Ningbo Daily, 2018 -03-15(011).)
[133] 崔趁欣, 李岩 . 河北金融[J].河北金融,2018(3):7-13.
[133] ( Cui Chenxin, Li Yan . Coupling Analysis and Application Prospect of Treasury Revenue and Economic Growth in the Era of Big Data: A Case Study of Hebei Province[J]. Hebei Finance, 2018(3):7-13.)
[134] Bok B, Caratelli D, Giannone D , et al. Macroeconomic Nowcasting and Forecasting with Big Data[J]. Annual Review of Economics, 2018,10:615-643.
[135] 百度数智平台——百度大数据智能力量[EB/OL]. [ 2020- 01- 11]. .
[135] ( Baidu DI——Baidu Big Data Intelligent Power [EB/OL]. [ 2020- 01- 11]. .)
[136] 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.
[137] Sobolevsky S, Massaro E, Bojic I, et al. Predicting Regional Economic Indices Using Big Data of Individual Bank Card Transactions [C]// Proceedings of the 2017 IEEE International Conference on Big Data. IEEE, 2017: 1313-1318.
[138] 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.
[138] ( Liu Taoxiong, Xu Xiaofei . Can Internet Search Behavior Help to Forecast the Macro Economy?[J]. Economic Research Journal, 2015,50(12):68-83.)
[139] Götz T B, Knetsch T A . Google Data in Bridge Equation Models for German GDP[J]. International Journal of Forecasting, 2019,35(1):45-66.
[140] 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. .
[141] D’Amuri F, Marcucci J . “Google It!” Forecasting the US Unemployment Rate with a Google Job Search Index[R].FEEM Working Paper No.31. 2010. .
[142] Artola C, Pinto F, de Pedraza García P . Can Internet Searches Forecast Tourism Inflows?[J]. International Journal of Manpower, 2015,36(1):103-116.
[143] 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.
[143] ( Arthur B. Complexity and the Economy[M]. Translated by Jia Yongmin. Hangzhou: Zhejiang People’s Publishing House, 2018.)
[144] 王国成 . 行为大数据与计算实验——探索经济研究新方法[J].天津社会科学, 2016(3):86-92.
[144] ( Wang Guocheng . Behavioral Big Data and Computational Experiments——Exploring New Methods for Economic Research[J]. Tianjin Social Sciences, 2016(3):86-92.)
[145] Hidalgo C A, Hausmann R . The Building Blocks of Economic Complexity[J]. Proceedings of the National Academy of Sciences, 2009,106(26):10570-10575.
[146] Gao J, Zhou T . Quantifying China’s Regional Economic Complexity[J]. Physica A: Statistical Mechanics and Its Applications, 2018,492:1591.
[147] 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.
[148] Cristelli M, Tacchella A, Pietronero L . The Heterogeneous Dynamics of Economic Complexity[J]. PLoS ONE, 2015,10(2):e0117174.
[149] Tacchella A, Cristelli M, Caldarelli G, et al. A New Metrics for Countries’ Fitness and Products’ Complexity[J]. Scientific Reports , 2012, 2: Article No. 723.
[150] Attewell P, Monaghan D, Kwong D. Data Mining for the Social Sciences: An Introduction[M]. University of California Press, 2015.
[151] 张涛, 刘宽斌 . 社会科学文摘[J].社会科学文摘,2018(8):47-49.
[151] ( Zhang Tao, Liu Kuanbin . Application of “Big Data” in Macroeconomic Forecast Analysis[J]. Social Sciences Digest, 2018(8):47-49.)
[152] 冯·诺依曼 . 数学在科学和社会中的作用[M]. 程钊, 王丽霞,杨静译. 大连:大连理工大学出版社, 2009: 54-58.
[152] ( Von Neumann J. The Role of Mathematics in the Science and in Society[M]. Translated by Cheng Zhao, Wang Lixia, Yang Jing. Dalian: Dalian University of Technology Press, 2009: 54-58.)
[153] 信息化与网络经济——访中国信息协会副会长乌家培教授[EB/OL]. [ 2019- 12- 01]. .
[153] ( Informatization and Network Economy——Interview with Professor Wu Jiapei, Vice Chairman of China Information Association[EB/OL]. [ 2019- 12- 01]. .)
[154] 隆云滔, 李洪涛, 王国成 . 基于主体的建模方法与宏观经济政策分析[J]. 经济与管理, 2018,32(2):47-52,80.
[154] ( Long Yuntao, Li Hongtao, Wang Guocheng . Agent-based Modeling and Macroeconomic Policy Analysis[J]. Economy and Management, 2018,32(2):47-52,80.)
[155] Anderson C . The End of Theory: The Data Deluge Makes the Scientific Method Obsolete[J/OL]. Wired Magazine, 2008, 16(7).[ 2019- 12- 01]. .
[156] 蔡永鸿, 唐斯琪, 于娟 . 大数据时代宏观经济与微观企业的行为联接[J].中国市场, 2015(8):88-89.
[156] ( Cai Yonghong, Tang Siqi, Yu Juan . Macro-economic and Micro-enterprise Behavior Connection in the Era of Big Data[J].China Market, 2015(8):88-89.)
[157] 赵怡君 . 全国流通经济[J].全国流通经济,2018(8):75-76.
[157] ( Zhao Yijun . Discussion on the Behavioral Connection Between Macro Economy and Micro Enterprises in the Era of Big Data[J]. China Circulation Economy, 2018(8):75-76.)
[1] Chang Zhijun,Qian Li,Xie Jing,Wu Zhenxin,Zhang Hu,Yu Qianqian,Wang Ying,Wang Yongji. Big Data Platform for Sci-Tech Literature Based on Distributed Technology[J]. 数据分析与知识发现, 2021, 5(3): 69-77.
[2] Chen Shiji, Qiu Junping, Yu Bo. Topic Analysis of LIS Big Data Research with Overlay Mapping[J]. 数据分析与知识发现, 2021, 5(10): 51-59.
[3] Zhao Yuxiang,Lian Jingwen. Review of Cultural Heritage Crowdsourcing in the Domain of Digital Humanities[J]. 数据分析与知识发现, 2021, 5(1): 36-55.
[4] Qiu Erli,He Hongwei,Yi Chengqi,Li Huiying. Research on Public Policy Support Based on Character-level CNN Technology[J]. 数据分析与知识发现, 2020, 4(7): 28-37.
[5] Wang Jiandong,Yu Shiyang. Principles on Constructing National Economic Brain[J]. 数据分析与知识发现, 2020, 4(7): 2-17.
[6] Beibei Kong,Jing Xie,Li Qian,Zhijun Chang,Zhenxin Wu. Methodology and Tools to Enrich Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(7): 113-122.
[7] Xiaozhou Dong,Xinkang Chen. E-Coupon and Economic Performance of E-commerce[J]. 数据分析与知识发现, 2019, 3(6): 42-49.
[8] Quan Lu,Anqi Zhu,Jiyue Zhang,Jing Chen. Research on User Information Requirement in Chinese Network Health Community: Taking Tumor-forum Data of Qiuyi as an Example[J]. 数据分析与知识发现, 2019, 3(4): 22-32.
[9] Ying Wang,Li Qian,Jing Xie,Zhijun Chang,Beibei Kong. Building Knowledge Graph with Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(1): 15-26.
[10] Li Qian,Jing Xie,Zhijun Chang,Zhenxin Wu,Dongrong Zhang. Designing Smart Knowledge Services with Sci-Tech Big Data[J]. 数据分析与知识发现, 2019, 3(1): 4-14.
[11] Jiying Hu,Jing Xie,Li Qian,Changlei Fu. Constructing Big Data Platform for Sci-Tech Knowledge Discovery with Knowledge Graph[J]. 数据分析与知识发现, 2019, 3(1): 55-62.
[12] Jing Xie,Li Qian,Hongbo Shi,Beibei Kong,Jiying Hu. Designing Framework for Precise Service of Scholarly Big Data[J]. 数据分析与知识发现, 2019, 3(1): 63-71.
[13] Shen Zhihong,Yao Chang,Hou Yanfei,Wu Linhuan,Li Yuepeng. Big Linked Data Management: Challenges, Solutions and Practices[J]. 数据分析与知识发现, 2018, 2(1): 9-20.
[14] Yang Cao,Wenfei Fan,Tengfei Yuan. Is Big Data Analytics Beyond the Reach of Small Companies?[J]. 数据分析与知识发现, 2017, 1(9): 1-7.
[15] Chao Lemen,Yang Canjun,Wang Shengjie,Zhao Junpeng,Xu Mengtian. Data Science Curriculums Around the World: An Empirical Study[J]. 数据分析与知识发现, 2017, 1(6): 12-21.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626,82624938