Sources and Types of Big Data for Macroeconomic Forecasting
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- Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008.
"Nowcasting: The real-time informational content of macroeconomic data,"
Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
- D’Amuri, Francesco & Marcucci, Juri, 2017.
"The predictive power of Google searches in forecasting US unemployment,"
International Journal of Forecasting, Elsevier, vol. 33(4), pages 801-816.
- Francesco D'Amuri & Juri Marcucci, 2012. "The predictive power of Google searches in forecasting unemployment," Temi di discussione (Economic working papers) 891, Bank of Italy, Economic Research and International Relations Area.
- Pan, Zhiyuan & Wang, Qing & Wang, Yudong & Yang, Li, 2018. "Forecasting U.S. real GDP using oil prices: A time-varying parameter MIDAS model," Energy Economics, Elsevier, vol. 72(C), pages 177-187.
- Bańbura, Marta & Giannone, Domenico & Modugno, Michele & Reichlin, Lucrezia, 2013.
"Now-Casting and the Real-Time Data Flow,"
Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 195-237,
Elsevier.
- Reichlin, Lucrezia & Giannone, Domenico & Modugno, Michele & Banbura, Marta, 2012. "Now-casting and the real-time data flow," CEPR Discussion Papers 9112, C.E.P.R. Discussion Papers.
- Giannone, Domenico & Reichlin, Lucrezia & Bańbura, Marta & Modugno, Michele, 2013. "Now-casting and the real-time data flow," Working Paper Series 1564, European Central Bank.
- Martha Banbura & Domenico Giannone & Michèle Modugno & Lucrezia Reichlin, 2012. "Now-Casting and the Real-Time Data Flow," Working Papers ECARES ECARES 2012-026, ULB -- Universite Libre de Bruxelles.
- Ghysels, Eric & Ozkan, Nazire, 2015. "Real-time forecasting of the US federal government budget: A simple mixed frequency data regression approach," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1009-1020.
- Norman R. Swanson & Weiqi Xiong, 2018.
"Big data analytics in economics: What have we learned so far, and where should we go from here?,"
Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 51(3), pages 695-746, August.
- Norman R. Swanson & Weiqi Xiong, 2018. "Big data analytics in economics: What have we learned so far, and where should we go from here?," Canadian Journal of Economics, Canadian Economics Association, vol. 51(3), pages 695-746, August.
- Laszlo Matyas (ed.), 2017. "The Econometrics of Multi-dimensional Panels," Advanced Studies in Theoretical and Applied Econometrics, Springer, number 978-3-319-60783-2.
- Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015.
"Do high-frequency financial data help forecast oil prices? The MIDAS touch at work,"
International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
- Kilian, Lutz & Baumeister, Christiane, 2013. "Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work," CEPR Discussion Papers 9768, C.E.P.R. Discussion Papers.
- Christiane Baumeister & Pierre Guérin & Lutz Kilian, 2014. "Do High-Frequency Financial Data Help Forecast Oil Prices? The MIDAS Touch at Work," Staff Working Papers 14-11, Bank of Canada.
- Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2013. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," CFS Working Paper Series 2013/22, Center for Financial Studies (CFS).
- David Bholat & Stephen Hans & Pedro Santos & Cheryl Schonhardt-Bailey, 2015. "Text mining for central banks," Handbooks, Centre for Central Banking Studies, Bank of England, number 33, April.
- Libero Monteforte & Gianluca Moretti, 2013.
"Real‐Time Forecasts of Inflation: The Role of Financial Variables,"
Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(1), pages 51-61, January.
- Libero Monteforte & Gianluca Moretti, "undated". "Real time forecasts of inflation: the role of financial variables," Working Papers wp2011-6, Department of the Treasury, Ministry of the Economy and of Finance.
- Libero Monteforte & Gianluca Moretti, 2010. "Real time forecasts of inflation: the role of financial variables," Temi di discussione (Economic working papers) 767, Bank of Italy, Economic Research and International Relations Area.
- Roberto Rigobón, 2015. "Presidential Address: Macroeconomics and Online Prices," Economía Journal, The Latin American and Caribbean Economic Association - LACEA, vol. 0(Spring 20), pages 199-213, February.
- Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013.
"Should Macroeconomic Forecasters Use Daily Financial Data and How?,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
- Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should macroeconomic forecasters use daily financial data and how?," University of Cyprus Working Papers in Economics 09-2010, University of Cyprus Department of Economics.
- Eric Ghysels & Andros Kourtellos & Elena Andreou, 2012. "Should macroeconomic forecasters use daily financial data and how?," 2012 Meeting Papers 1196, Society for Economic Dynamics.
- Elena Andreou & Eric Ghysels & Andros Kourtellos, 2010. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Working Paper series 42_10, Rimini Centre for Economic Analysis.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016.
"Measuring Economic Policy Uncertainty,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," Economics Working Papers 15111, Hoover Institution, Stanford University.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," NBER Working Papers 21633, National Bureau of Economic Research, Inc.
- Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2015. "Measuring Economic Policy Uncertainty," CEP Discussion Papers dp1379, Centre for Economic Performance, LSE.
- Baker, Scott R. & Bloom, Nicholas & Davis, Steven J., 2015. "Measuring economic policy uncertainty," LSE Research Online Documents on Economics 64986, London School of Economics and Political Science, LSE Library.
- Davis, Steven & Bloom, Nicholas & Baker, Scott, 2015. "Measuring Economic Policy Uncertainty," CEPR Discussion Papers 10900, C.E.P.R. Discussion Papers.
- Modugno, Michele, 2013.
"Now-casting inflation using high frequency data,"
International Journal of Forecasting, Elsevier, vol. 29(4), pages 664-675.
- Modugno, Michele, 2011. "Nowcasting inflation using high frequency data," Working Paper Series 1324, European Central Bank.
- Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2018.
"Macroeconomic Nowcasting and Forecasting with Big Data,"
Annual Review of Economics, Annual Reviews, vol. 10(1), pages 615-643, August.
- Brandyn Bok & Daniele Caratelli & Domenico Giannone & Argia M. Sbordone & Andrea Tambalotti, 2017. "Macroeconomic nowcasting and forecasting with big data," Staff Reports 830, Federal Reserve Bank of New York.
- Giannone, Domenico & Tambalotti, Andrea & Sbordone, Argia & Bok, Brandyn & Caratelli, Daniele, 2018. "Macroeconomic Nowcasting and Forecasting with Big Data," CEPR Discussion Papers 12589, C.E.P.R. Discussion Papers.
- Reichlin, Lucrezia & Giannone, Domenico & Small, David, 2005.
"Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases,"
CEPR Discussion Papers
5178, C.E.P.R. Discussion Papers.
- Giannone, Domenico & Reichlin, Lucrezia & Small, David H., 2006. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Working Paper Series 633, European Central Bank.
- Domenico Giannone & Lucrezia Reichlin & David H. Small, 2005. "Nowcasting GDP and inflation: the real-time informational content of macroeconomic data releases," Finance and Economics Discussion Series 2005-42, Board of Governors of the Federal Reserve System (U.S.).
- Domenico Giannone & Lucrezia Reichlin & David H Small, 2007. "Nowcasting GDP and Inflation: The Real-Time Informational Content of Macroeconomic Data Releases," Money Macro and Finance (MMF) Research Group Conference 2006 164, Money Macro and Finance Research Group.
- Cavallo, Alberto, 2013. "Online and official price indexes: Measuring Argentina's inflation," Journal of Monetary Economics, Elsevier, vol. 60(2), pages 152-165.
- Nicoletta Berardi & Patrick Sevestre & Jonathan Thébault, 2017.
"The Determinants of Consumer Price Dispersion: Evidence from French Supermarkets,"
Post-Print
hal-01685367, HAL.
- N. Berardi & P. Sevestre & J. Thébault, 2017. "The Determinants of Consumer Price Dispersion: Evidence from French Supermarkets," Working papers 632, Banque de France.
- Aruoba, S. BoraÄŸan & Diebold, Francis X. & Scotti, Chiara, 2009.
"Real-Time Measurement of Business Conditions,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 417-427.
- Chiara Scotti & S.Boragan Aruoba & Francis X. Diebold & University of Maryland, 2006. "Real-Time Measurement of Business Conditions," Computing in Economics and Finance 2006 387, Society for Computational Economics.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-Time Measurement of Business Conditions," NBER Working Papers 14349, National Bureau of Economic Research, Inc.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-Time Measurement of Business Conditions," PIER Working Paper Archive 07-028, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2008. "Real-time measurement of business conditions," Working Papers 08-19, Federal Reserve Bank of Philadelphia.
- S. Boragan Aruoba & Francis X. Diebold & Chiara Scotti, 2007. "Real-time measurement of business conditions," International Finance Discussion Papers 901, Board of Governors of the Federal Reserve System (U.S.).
- Ivancic, Lorraine & Erwin Diewert, W. & Fox, Kevin J., 2011.
"Scanner data, time aggregation and the construction of price indexes,"
Journal of Econometrics, Elsevier, vol. 161(1), pages 24-35, March.
- Diewert, Erwin & Fox, Kevin J. & Ivancic, Lorraine, 2009. "Scanner Data, Time Aggregation and the Construction of Price Indexes," Economics working papers erwin_diewert-2009-48, Vancouver School of Economics, revised 22 Sep 2009.
- Dean Croushore, 2011.
"Frontiers of Real-Time Data Analysis,"
Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
- Dean Croushore, 2008. "Frontiers of real-time data analysis," Working Papers 08-4, Federal Reserve Bank of Philadelphia.
- Leif Anders Thorsrud, 2020.
"Words are the New Numbers: A Newsy Coincident Index of the Business Cycle,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(2), pages 393-409, April.
- Leif Anders Thorsrud, 2016. "Words are the new numbers: A newsy coincident index of business cycles," Working Papers No 4/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Leif Anders Thorsrud, 2016. "Words are the new numbers: A newsy coincident index of business cycles," Working Paper 2016/21, Norges Bank.
- Karen C. Seto & Robert K. Kaufmann, 2003. "Modeling the Drivers of Urban Land Use Change in the Pearl River Delta, China: Integrating Remote Sensing with Socioeconomic Data," Land Economics, University of Wisconsin Press, vol. 79(1), pages 106-121.
- Marian Alexander Dietzel & Nicole Braun & Wolfgang Schäfers, 2014. "Sentiment-based commercial real estate forecasting with Google search volume data," Journal of Property Investment & Finance, Emerald Group Publishing Limited, vol. 32(6), pages 540-569, August.
- Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
- Ericsson, Neil R., 2016.
"Eliciting GDP forecasts from the FOMC’s minutes around the financial crisis,"
International Journal of Forecasting, Elsevier, vol. 32(2), pages 571-583.
- Neil R. Ericsson, 2015. "Eliciting GDP Forecasts from the FOMC’s Minutes Around the Financial Crisis," Working Papers 2015-003, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
- Neil R. Ericsson, 2015. "Eliciting GDP Forecasts from the FOMC’s Minutes Around the Financial Crisis," International Finance Discussion Papers 1152, Board of Governors of the Federal Reserve System (U.S.).
- Neil R. Ericsson, 2017.
"Predicting Fed Forecasts,"
Journal of Reviews on Global Economics, Lifescience Global, vol. 6, pages 175-180.
- Neil R. Ericsson, 2016. "Predicting Fed Forecasts," IFDP Notes 2016-02-12, Board of Governors of the Federal Reserve System (U.S.).
- Tefft, Nathan, 2011. "Insights on unemployment, unemployment insurance, and mental health," Journal of Health Economics, Elsevier, vol. 30(2), pages 258-264, March.
- Márton Mestyán & Taha Yasseri & János Kertész, 2013. "Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-8, August.
- Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
- Rajagopal, 2014.
"The Human Factors,"
Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249,
Palgrave Macmillan.
- Rajagopal, 2013. "The Human Factors," Palgrave Macmillan Books, in: Managing Social Media and Consumerism, chapter 9, pages 173-194, Palgrave Macmillan.
- Silver, Mick & Heravi, Saeed, 2001. "Scanner Data and the Measurement of Inflation," Economic Journal, Royal Economic Society, vol. 111(472), pages 383-404, June.
- Marian Alexander Dietzel & Nicole Braun & Wolfgang Schäfers, 2014. "Sentiment-Based Commercial Real Estate Forecasting with Google Search Volume Data," ERES eres2014_17, European Real Estate Society (ERES).
- Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
- Alasdair Brown & Dooruj Rambaccussing & J. James Reade & Giambattista Rossi, 2018. "Forecasting With Social Media: Evidence From Tweets On Soccer Matches," Economic Inquiry, Western Economic Association International, vol. 56(3), pages 1748-1763, July.
- Sonal S. Pandya & Rajkumar Venkatesan, 2016. "French Roast: Consumer Response to International Con flict--Evidence from Supermarket Scanner Data," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 42-56, March.
- Francis X. Diebold, 2012. "On the Origin(s) and Development of the Term “Big Data"," PIER Working Paper Archive 12-037, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
- Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
- Leighton Vaughan Williams & J. James Reade, 2016. "Prediction Markets, Social Media and Information Efficiency," Kyklos, Wiley Blackwell, vol. 69(3), pages 518-556, August.
- Jeremy Ginsberg & Matthew H. Mohebbi & Rajan S. Patel & Lynnette Brammer & Mark S. Smolinski & Larry Brilliant, 2009. "Detecting influenza epidemics using search engine query data," Nature, Nature, vol. 457(7232), pages 1012-1014, February.
- Paul Smith, 2016. "Google's MIDAS Touch: Predicting UK Unemployment with Internet Search Data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(3), pages 263-284, April.
- Alberto Cavallo & Roberto Rigobon, 2016.
"The Billion Prices Project: Using Online Prices for Measurement and Research,"
Journal of Economic Perspectives, American Economic Association, vol. 30(2), pages 151-178, Spring.
- Alberto Cavallo & Roberto Rigobon, 2016. "The Billion Prices Project: Using Online Prices for Measurement and Research," NBER Working Papers 22111, National Bureau of Economic Research, Inc.
- Alberto Abadie & Susan Athey & Guido W. Imbens & Jeffrey M. Wooldridge, 2014. "Finite Population Causal Standard Errors," NBER Working Papers 20325, National Bureau of Economic Research, Inc.
- Austan D. Goolsbee & Peter J. Klenow, 2018. "Internet Rising, Prices Falling: Measuring Inflation in a World of E-Commerce," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 488-492, May.
- repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
- Keola, Souknilanh & Andersson, Magnus & Hall, Ola, 2015. "Monitoring Economic Development from Space: Using Nighttime Light and Land Cover Data to Measure Economic Growth," World Development, Elsevier, vol. 66(C), pages 322-334.
- Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
- Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December.
- repec:arz:wpaper:eres2014-17 is not listed on IDEAS
- Austan D. Goolsbee & Peter J. Klenow, 2018. "Internet Rising, Prices Falling: Measuring Inflation in a World of E-Commerce," NBER Working Papers 24649, National Bureau of Economic Research, Inc.
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Keywords
big data; data sources;JEL classification:
- C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
NEP fields
This paper has been announced in the following NEP Reports:- NEP-BIG-2019-07-15 (Big Data)
- NEP-MAC-2019-07-15 (Macroeconomics)
- NEP-PAY-2019-07-15 (Payment Systems and Financial Technology)
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