Incorporation of Social Media Data into Macroeconomic Forecast Systems: A Mixed Frequency Modelling Approach

Macroeconomic forecasts enable the policy-makers to foresee the future economic trends and take prompt measures to ensure longer economic growth and quicker economic recovery. Accurate and timely macroeconomic forecasts may also help the enterprises to make better long-term business strategies. Social media has a swift and sensitive response to economic dynamics through online news reports, interviews and individual comments. Economic Indices extracted from social media data are more immediate and comprehensive, but lack of stability and credibility in empirical studies. This research proposed a mix frequency modelling approach to incorporate only the recent high frequency part of social media data in traditional econometrics based macroeconomic forecasting with support of a multisource based macroeconomic forecast system. A mixed data sampling (MIDAS) model is constructed and an empirical evaluation is presented to show how to incorporate Google search queries into Chinese CPI forecasting. The empirical results indicate a satisfactory improvement in forecasting performance. The multisource modelling and forecasting framework offers a practical and implementable solution for involving social media data sources into macroeconomic forecasting systems. This research contributes to future development of decision support systems for governmental policy-making and enterprises’ operational decisions in the Big Data era.

[1]  David M. Pennock,et al.  Predicting consumer behavior with Web search , 2010, Proceedings of the National Academy of Sciences.

[2]  Kin Keung Lai,et al.  An Integrated Decision Support Framework for Macroeconomic Policy Making Based on Early Warning Theories , 2009, Int. J. Inf. Technol. Decis. Mak..

[3]  Konstantin A. Kholodilin,et al.  Do Google Searches Help in Nowcasting Private Consumption? A Real-Time Evidence for the US , 2010 .

[4]  Hyun-young Choi,et al.  Predicting Initial Claims for Unemployment Benefits , 2009 .

[5]  Stephen E. Robertson,et al.  Relevance weighting of search terms , 1976, J. Am. Soc. Inf. Sci..

[6]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[7]  E. Ghysels,et al.  There is a Risk-Return Tradeoff after All , 2004 .

[8]  C. Granger,et al.  Co-integration and error correction: representation, estimation and testing , 1987 .

[9]  M. E. Maron,et al.  On Relevance, Probabilistic Indexing and Information Retrieval , 1960, JACM.

[10]  E. Ghysels,et al.  MIDAS Regressions: Further Results and New Directions , 2006 .

[11]  Daniel E. O'Leary Social Media in DMSS System Development and Management , 2011, Int. J. Decis. Support Syst. Technol..

[12]  E. Ghysels,et al.  Série Scientifique Scientific Series Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies , 2022 .

[13]  N. Askitas,et al.  Google Econometrics and Unemployment Forecasting , 2009, SSRN Electronic Journal.

[14]  Hsinchun Chen,et al.  A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews , 2010, IEEE Intelligent Systems.

[15]  Jian-Yun Nie,et al.  Using query contexts in information retrieval , 2007, SIGIR.

[16]  Sumit Jain,et al.  An Approach for Comparative Research Between Ontology Building & Learning Tools for Information Extraction & Retrieval , 2012 .

[17]  Eleni Stroulia,et al.  Virtual worlds - past, present, and future: New directions in social computing , 2009, Decis. Support Syst..

[18]  Muhammad Abdul-Mageed,et al.  SAMAR: A System for Subjectivity and Sentiment Analysis of Arabic Social Media , 2012, WASSA@ACL.

[19]  Chien Chin Chen,et al.  Business Cycle Indication Using Query Logs of Search Engines , 2010, 2010 International Conference on P2P, Parallel, Grid, Cloud and Internet Computing.

[20]  Ali Azadeh,et al.  An intelligent decision support system for forecasting and optimization of complex personnel attributes in a large bank , 2012, Expert Syst. Appl..

[21]  Torsten Schmidt,et al.  A monthly consumption indicator for Germany based on Internet search query data , 2010 .

[22]  Daniel L. Sherrell,et al.  Communications of the Association for Information Systems , 1999 .

[23]  John F. MacGregor,et al.  Some Recent Advances in Forecasting and Control , 1968 .

[24]  David H. Small,et al.  Nowcasting: the real time informational content of macroeconomic data releases , 2008 .

[25]  Giselle C. Guzman,et al.  Internet Search Behavior as an Economic Forecasting Tool: The Case of Inflation Expectations , 2011 .

[26]  Rachana Shanbhogue,et al.  Using Internet Search Data as Economic Indicators , 2011 .

[27]  Tuomo Kakkonen,et al.  Towards SoMEST – Combining Social Media Monitoring with Event Extraction and Timeline Analysis , 2012 .

[28]  Doug Schuler,et al.  Social computing , 1994, CACM.

[29]  Andrew B. Whinston,et al.  Social Computing: An Overview , 2007, Commun. Assoc. Inf. Syst..

[30]  Wenji Mao,et al.  Social Computing: From Social Informatics to Social Intelligence , 2007, IEEE Intell. Syst..

[31]  Frank T. Magiera,et al.  There Is a Risk–Return Trade-Off After All , 2005 .

[32]  Marco Lippi,et al.  The Generalized Dynamic Factor Model , 2002 .

[33]  H. Varian,et al.  Predicting the Present with Google Trends , 2009 .

[34]  Claudio Carpineto,et al.  A Survey of Automatic Query Expansion in Information Retrieval , 2012, CSUR.

[35]  Ricardo Baeza-Yates,et al.  Information Retrieval: Data Structures and Algorithms , 1992 .

[36]  Michael T. Owyang,et al.  Forecasting with Mixed Frequencies , 2010 .

[37]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[38]  Michael W. Berry,et al.  Survey of Text Mining: Clustering, Classification, and Retrieval , 2007 .

[39]  Yan Carrière-Swallow,et al.  Nowcasting With Google Trends in an Emerging Market , 2013 .

[40]  Bernardo A. Huberman,et al.  Predicting the Future with Social Media , 2010, Web Intelligence.

[41]  Venkata Subramaniam,et al.  Information Retrieval: Data Structures & Algorithms , 1992 .

[42]  Desheng Dash Wu,et al.  Using text mining and sentiment analysis for online forums hotspot detection and forecast , 2010, Decis. Support Syst..

[43]  John H. Gerdes,et al.  Using web-based search data to predict macroeconomic statistics , 2005, CACM.