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Google Econometrics and Unemployment Forecasting

Citations

Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Measuring unemployment with Google
    by Economic Logician in Economic Logic on 2009-07-01 13:02:00

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:

  1. Christian Hutter & Enzo Weber, 2015. "Constructing a new leading indicator for unemployment from a survey among German employment agencies," Applied Economics, Taylor & Francis Journals, vol. 47(33), pages 3540-3558, July.
  2. Pete Richardson, 2018. "Nowcasting and the Use of Big Data in Short-Term Macroeconomic Forecasting: A Critical Review," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Études Économiques (INSEE), issue 505-506, pages 65-87.
  3. Artem Meshcheryakov & Stoyu I Ivanov, 2017. "Investor's sentiment in predicting the Effective Federal Funds Rate," Economics Bulletin, AccessEcon, vol. 37(4), pages 2767-2796.
  4. Jichang Dong & Wei Dai & Ying Liu & Lean Yu & Jie Wang, 2019. "Forecasting Chinese Stock Market Prices using Baidu Search Index with a Learning-Based Data Collection Method," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(05), pages 1605-1629, September.
  5. John W Ayers & Kurt Ribisl & John S Brownstein, 2011. "Using Search Query Surveillance to Monitor Tax Avoidance and Smoking Cessation following the United States' 2009 “SCHIP” Cigarette Tax Increase," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-7, March.
  6. Andree Ehlert & Jan Seidel & Ursula Weisenfeld, 2020. "Trouble on my mind: the effect of catastrophic events on people’s worries," Empirical Economics, Springer, vol. 59(2), pages 951-975, August.
  7. repec:zbw:rwirep:0382 is not listed on IDEAS
  8. Jorge M. Agüero & Trinidad Beleche, 2016. "Health Shocks and the Long-Lasting Change in Health Behaviors: Evidence from Mexico," Working papers 2016-26, University of Connecticut, Department of Economics.
  9. Yann Algan & Fabrice Murtin & Elizabeth Beasley & Kazuhito Higa & Claudia Senik, 2019. "Well-being through the lens of the internet," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-23, January.
  10. Nathan, Max & Rosso, Anna, 2015. "Mapping digital businesses with big data: Some early findings from the UK," Research Policy, Elsevier, vol. 44(9), pages 1714-1733.
  11. Tong Liu & Guojun He & Alexis Lau, 2018. "Avoidance behavior against air pollution: evidence from online search indices for anti-PM2.5 masks and air filters in Chinese cities," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 20(2), pages 325-363, April.
  12. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
  13. Brian Fabo & Miroslav Beblavý & Karolien Lenaerts, 2017. "The importance of foreign language skills in the labour markets of Central and Eastern Europe: assessment based on data from online job portals," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(3), pages 487-508, August.
  14. Jorge M. Agüero, 2019. "Information and Behavioral Responses with More than One Agent: The Case of Domestic Violence Awareness Campaigns," Working papers 2019-04, University of Connecticut, Department of Economics.
  15. Jaroslav Pavlicek & Ladislav Kristoufek, 2015. "Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-11, May.
  16. Juan Camilo Anzoátegui-Zapata & Juan Camilo Galvis-Ciro, 2020. "Disagreements in Consumer Inflation Expectations: Empirical Evidence for a Latin American Economy," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 16(2), pages 99-122, November.
  17. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing, vol. 36(1), pages 2-12, April.
  18. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
  19. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
  20. VAN DER WIELEN Wouter & BARRIOS Salvador, 2020. "Fear and Employment During the COVID Pandemic: Evidence from Search Behaviour in the EU," JRC Working Papers on Taxation & Structural Reforms 2020-08, Joint Research Centre (Seville site).
  21. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
  22. David Kohns & Arnab Bhattacharjee, 2020. "Developments on the Bayesian Structural Time Series Model: Trending Growth," Papers 2011.00938, arXiv.org.
  23. Luca Bonacini & Giovanni Gallo & Fabrizio Patriarca, 2021. "Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 275-301, January.
  24. Semen Son-Turan, 2016. "The Impact of Investor Sentiment on the "Leverage Effect"," International Econometric Review (IER), Econometric Research Association, vol. 8(1), pages 4-18, April.
  25. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
  26. Torsten Schmidt & Simeon Vosen, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 0382, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  27. Fantazzini, Dean & Toktamysova, Zhamal, 2015. "Forecasting German car sales using Google data and multivariate models," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 97-135.
  28. Kholodilin, Konstantin A. & Siliverstovs, Boriss, 2012. "Measuring regional inequality by internet car price advertisements: Evidence for Germany," Economics Letters, Elsevier, vol. 116(3), pages 414-417.
  29. Han Wang & Geng Peng & Benfu Lv, 2018. "Effect of Retail Investor Attention on Chinas A-Share Market Under a Strengthening Financial Regulatory Policy," Asian Economic and Financial Review, Asian Economic and Social Society, vol. 8(10), pages 1274-1297, October.
  30. repec:zbw:rwirep:0155 is not listed on IDEAS
  31. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
  32. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 2020. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 59(6), pages 2821-2839, December.
  33. Melody Y. Huang & Randall R. Rojas & Patrick D. Convery, 0. "Forecasting stock market movements using Google Trend searches," Empirical Economics, Springer, vol. 0, pages 1-19.
  34. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
  35. Park, Sungjun & Kim, Jinsoo, 2018. "The effect of interest in renewable energy on US household electricity consumption: An analysis using Google Trends data," Renewable Energy, Elsevier, vol. 127(C), pages 1004-1010.
  36. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
  37. Christoph Safferling & Aaron Lowen, 2011. "Economics in the Kingdom of Loathing: Analysis of Virtual Market Data," Working Paper Series of the Department of Economics, University of Konstanz 2011-30, Department of Economics, University of Konstanz.
  38. Böhme, Marcus H. & Gröger, André & Stöhr, Tobias, 2020. "Searching for a better life: Predicting international migration with online search keywords," Journal of Development Economics, Elsevier, vol. 142(C).
  39. Nagao, Shintaro & Takeda, Fumiko & Tanaka, Riku, 2019. "Nowcasting of the U.S. unemployment rate using Google Trends," Finance Research Letters, Elsevier, vol. 30(C), pages 103-109.
  40. Nikos Askitas & Klaus F. Zimmermann, 2009. "Prognosen aus dem Internet: weitere Erholung am Arbeitsmarkt erwartet," DIW Wochenbericht, DIW Berlin, German Institute for Economic Research, vol. 76(25), pages 402-408.
  41. Fabio Milani, 2021. "COVID-19 outbreak, social response, and early economic effects: a global VAR analysis of cross-country interdependencies," Journal of Population Economics, Springer;European Society for Population Economics, vol. 34(1), pages 223-252, January.
  42. Agüero, Jorge M. & Beleche, Trinidad, 2017. "Health shocks and their long-lasting impact on health behaviors: Evidence from the 2009 H1N1 pandemic in Mexico," Journal of Health Economics, Elsevier, vol. 54(C), pages 40-55.
  43. Hou, Xiaohui & Gao, Zhixian & Wang, Qing, 2016. "Internet finance development and banking market discipline: Evidence from China," Journal of Financial Stability, Elsevier, vol. 22(C), pages 88-100.
  44. Levent Bulut, 2015. "Google Trends and Forecasting Performance of Exchange Rate Models," IPEK Working Papers 1505, Ipek University, Department of Economics.
  45. Kovács, Olivér, 2017. "Az ipar 4.0 komplexitása - II [The Complexity of Industry 4.0 - Part 2]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(9), pages 970-987.
  46. Blazquez, Desamparados & Domenech, Josep, 2018. "Big Data sources and methods for social and economic analyses," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 99-113.
  47. Georgios Bampinas & Theodore Panagiotidis & Christina Rouska, 2019. "Volatility persistence and asymmetry under the microscope: the role of information demand for gold and oil," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(1), pages 180-197, February.
  48. Mohamed Arouri & Amal Aouadi & Philippe Foulquier & Frédéric Teulon, 2013. "Can Information Demand Help to Predict Stock Market Liquidity ? Google it !," Working Papers 2013-24, Department of Research, Ipag Business School.
  49. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 683-687.
  50. Dorinth W. van Dijk & Marc K. Francke, 2018. "Internet Search Behavior, Liquidity and Prices in the Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 46(2), pages 368-403, June.
  51. F. Antolini & L. Grassini, 2019. "Foreign arrivals nowcasting in Italy with Google Trends data," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(5), pages 2385-2401, September.
  52. Thomas Dimpfl & Tobias Langen, 2019. "How Unemployment Affects Bond Prices: A Mixed Frequency Google Nowcasting Approach," Computational Economics, Springer;Society for Computational Economics, vol. 54(2), pages 551-573, August.
  53. Heather R. Tierney & Bing Pan, 2012. "A poisson regression examination of the relationship between website traffic and search engine queries," Netnomics, Springer, vol. 13(3), pages 155-189, October.
  54. Huang, Xiankai & Zhang, Lifeng & Ding, Yusi, 2017. "The Baidu Index: Uses in predicting tourism flows –A case study of the Forbidden City," Tourism Management, Elsevier, vol. 58(C), pages 301-306.
  55. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
  56. Dimpfl, Thomas & Langen, Tobias, 2015. "A Cross-Country Analysis of Unemployment and Bonds with Long-Memory Relations," VfS Annual Conference 2015 (Muenster): Economic Development - Theory and Policy 112921, Verein für Socialpolitik / German Economic Association.
  57. Tefft, Nathan, 2011. "Insights on unemployment, unemployment insurance, and mental health," Journal of Health Economics, Elsevier, vol. 30(2), pages 258-264, March.
  58. Mirko Seithe & Lena Calahorrano, 2014. "Analysing Party Preferences Using Google Trends," CESifo Working Paper Series 4631, CESifo.
  59. Michał Chojnowski & Piotr Dybka, 2017. "Is Exchange Rate Moody? Forecasting Exchange Rate with Google Trends Data," Econometric Research in Finance, SGH Warsaw School of Economics, Collegium of Economic Analysis, vol. 2(1), pages 1-21, June.
  60. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
  61. Mihaela Simionescu & Dalia Streimikiene & Wadim Strielkowski, 2020. "What Does Google Trends Tell Us about the Impact of Brexit on the Unemployment Rate in the UK?," Sustainability, MDPI, Open Access Journal, vol. 12(3), pages 1-10, January.
  62. Li, Xin & Ma, Jian & Wang, Shouyang & Zhang, Xun, 2015. "How does Google search affect trader positions and crude oil prices?," Economic Modelling, Elsevier, vol. 49(C), pages 162-171.
  63. David Iselin & Boriss Siliverstovs, 2016. "Using newspapers for tracking the business cycle: a comparative study for Germany and Switzerland," Applied Economics, Taylor & Francis Journals, vol. 48(12), pages 1103-1118, March.
  64. Cheng, Maoyong & Qu, Yang, 2020. "Does bank FinTech reduce credit risk? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).
  65. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
  66. Latoeiro, Pedro & Veiga, Helena & Ramos, Sofía B., 2013. "Predictability of stock market activity using Google search queries," DES - Working Papers. Statistics and Econometrics. WS ws130605, Universidad Carlos III de Madrid. Departamento de Estadística.
  67. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW).
  68. D'Amuri, Francesco & Marcucci, Juri, 2009. "'Google it!' Forecasting the US unemployment rate with a Google job search index," ISER Working Paper Series 2009-32, Institute for Social and Economic Research.
  69. Schmitz, Sebastian, 2017. "The effects of Germany's new minimum wage on employment and welfare dependency," Discussion Papers 2017/21, Free University Berlin, School of Business & Economics.
  70. Smith, Geoffrey Peter, 2012. "Google Internet search activity and volatility prediction in the market for foreign currency," Finance Research Letters, Elsevier, vol. 9(2), pages 103-110.
  71. Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera, 2020. "Can online search data improve the forecast accuracy of pork price in China?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(4), pages 671-686, July.
  72. Jan Goebel & Christian Krekel & Tim Tiefenbach & Nicholas R. Ziebarth, 2014. "Natural Disaster, Environmental Concerns, Well-Being and Policy Action," CINCH Working Paper Series 1405, Universitaet Duisburg-Essen, Competent in Competition and Health.
  73. Benedikt Maas, 2020. "Short‐term forecasting of the US unemployment rate," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 394-411, April.
  74. Semen Son Turan, 2014. "Internet Search Volume and Stock Return Volatility: The Case of Turkish Companies," Information Management and Business Review, AMH International, vol. 6(6), pages 317-328.
  75. Nuarpear Warn Lekfuangfu & Voraprapa Nakavachara & Paphatsorn Sawaengsuksant, 2017. "Glancing at Labour Market Mismatch with User-generated Internet Data," PIER Discussion Papers 53, Puey Ungphakorn Institute for Economic Research, revised Jan 2017.
  76. Calahorrano, Lena & Seithe, Mirko, 2014. "Analysing Party Preferences Using Google Trends," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100294, Verein für Socialpolitik / German Economic Association.
  77. Dimitrios Anastasiou & Zacharias Bragoudakis & Stelios Giannoulakis, 2020. "Perceived vs actual financial crisis and bank credit standards: is there any indication of self-fulfilling prophecy?," Working Papers 277, Bank of Greece.
  78. Yann Algan & Elizabeth Beasley & Florian Guyot & Kazuhito Higad & Fabrice Murtin & Claudia Senik, 2015. "Big Data Measures of Well-Being: Evidence from a Google Well-Being Index in the US," Sciences Po publications info:hdl:2441/5k53daedc28, Sciences Po.
  79. Grazia Biorci & Antonella Emina & Michelangelo Puliga & Lisa Sella & Gianna Vivaldo, 2016. "Tweet-tales: moods of socio-economic crisis?," Working Papers 04/2016, IMT Institute for Advanced Studies Lucca, revised Jul 2016.
  80. Bai, Lijuan & Yan, Xiangbin & Yu, Guang, 2019. "Impact of CEO media appearance on corporate performance in social media," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
  81. Bouoiyour, Jamal & Selmi, Refk & Tiwari, Aviral, 2014. "Is Bitcoin business income or speculative bubble? Unconditional vs. conditional frequency domain analysis," MPRA Paper 59595, University Library of Munich, Germany.
  82. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
  83. Döhrn, Roland & Mitze, Timo & Schmidt, Torsten & Tauchmann, Harald & Vosen, Simeon, 2010. "Analyse und Prognose des Spar- und Konsumverhaltens privater Haushalte: Endbericht - November 2010," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 69982, March.
  84. Kureková, Lucia Mýtna & Beblavy, Miroslav & Thum, Anna-Elisabeth, 2014. "Using Internet Data to Analyse the Labour Market: A Methodological Enquiry," IZA Discussion Papers 8555, Institute of Labor Economics (IZA).
  85. Vicente, María Rosalía & López-Menéndez, Ana J. & Pérez, Rigoberto, 2015. "Forecasting unemployment with internet search data: Does it help to improve predictions when job destruction is skyrocketing?," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 132-139.
  86. Karolien Lenaerts & Miroslav Beblavý & Brian Fabo, 2016. "Prospects for utilisation of non-vacancy Internet data in labour market analysis—an overview," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 5(1), pages 1-18, December.
  87. Dean Fantazzini, 2014. "Nowcasting and Forecasting the Monthly Food Stamps Data in the US Using Online Search Data," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-27, November.
  88. Askitas, Nikos & Zimmermann, Klaus F., 2009. "Googlemetrie und Arbeitsmarkt in der Wirtschaftskrise," IZA Standpunkte 17, Institute of Labor Economics (IZA).
  89. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
  90. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
  91. Jiawei Du, 2020. "A Research on Cross-sectional Return Dispersion and Volatility of US Stock Market during COVID-19," Papers 2007.11546, arXiv.org.
  92. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
  93. Ying Liu & Yibing Chen & Sheng Wu & Geng Peng & Benfu Lv, 2015. "Composite leading search index: a preprocessing method of internet search data for stock trends prediction," Annals of Operations Research, Springer, vol. 234(1), pages 77-94, November.
  94. repec:ipg:wpaper:24 is not listed on IDEAS
  95. Pincheira, Pablo & Hernández, Ana María, 2019. "Forecasting Unemployment Rates with International Factors," MPRA Paper 97855, University Library of Munich, Germany.
  96. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
  97. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
  98. Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
  99. repec:ipg:wpaper:2013-024 is not listed on IDEAS
  100. Siliverstovs, Boriss & Wochner, Daniel S., 2018. "Google Trends and reality: Do the proportions match?," Journal of Economic Behavior & Organization, Elsevier, vol. 145(C), pages 1-23.
  101. Jianchun Fang & Wanshan Wu & Zhou Lu & Eunho Cho, 2019. "Using Baidu Index To Nowcast Mobile Phone Sales In China," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 64(01), pages 83-96, March.
  102. Maria De Paola & Vincenzo Scoppa, 2013. "Consumers’ Reactions to Negative Information on Product Quality: Evidence from Scanner Data," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 42(3), pages 235-280, May.
  103. Alessia Naccarato & Andrea Pierini & Stefano Falorsi, 2015. "Using Google Trend Data To Predict The Italian Unemployment Rate," Departmental Working Papers of Economics - University 'Roma Tre' 0203, Department of Economics - University Roma Tre.
  104. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute of Labor Economics (IZA), pages 206-206, November.
  105. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
  106. Schmidt, Torsten & Vosen, Simeon, 2012. "Using Internet Data to Account for Special Events in Economic Forecasting," Ruhr Economic Papers 382, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
  107. Yan Yan & Jiancheng Guan, 2019. "Entrepreneurial ecosystem, entrepreneurial rate and innovation: the moderating role of internet attention," International Entrepreneurship and Management Journal, Springer, vol. 15(2), pages 625-650, June.
  108. , 2020. "Using the Eye of the Storm to Predict the Wave of Covid-19 UI Claims," Working Paper Series WP 2020-10, Federal Reserve Bank of Chicago.
  109. Fondeur, Y. & Karamé, F., 2013. "Can Google data help predict French youth unemployment?," Economic Modelling, Elsevier, vol. 30(C), pages 117-125.
  110. 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.
  111. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
  112. Peter Kuhn, 2014. "The internet as a labor market matchmaker," IZA World of Labor, Institute of Labor Economics (IZA), pages 1-18, May.
  113. Maria De Paola & Vincenzo Scoppa & Valeria Pupo, 2014. "Absenteeism in the Italian Public Sector: The Effects of Changes in Sick Leave Policy," Journal of Labor Economics, University of Chicago Press, vol. 32(2), pages 337-360.
  114. Scheffel, Eric Michael, 2012. "Political uncertainty in a data-rich environment," MPRA Paper 37318, University Library of Munich, Germany.
  115. Jan Goebel & Christian Krekel & Tim Tiefenbach & Nicolas Ziebarth, 2015. "How natural disasters can affect environmental concerns, risk aversion, and even politics: evidence from Fukushima and three European countries," Journal of Population Economics, Springer;European Society for Population Economics, vol. 28(4), pages 1137-1180, October.
  116. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
  117. Bangwayo-Skeete, Prosper F. & Skeete, Ryan W., 2015. "Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach," Tourism Management, Elsevier, vol. 46(C), pages 454-464.
  118. Zeynalov, Ayaz, 2017. "Forecasting Tourist Arrivals in Prague: Google Econometrics," MPRA Paper 83268, University Library of Munich, Germany.
  119. Dimitrios Anastasiou & Konstantinos Drakos, 2021. "Nowcasting the Greek (semi‐) deposit run: Hidden uncertainty about the future currency in a Google search," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 1133-1150, January.
  120. repec:zbw:rwirep:0208 is not listed on IDEAS
  121. Naccarato, Alessia & Falorsi, Stefano & Loriga, Silvia & Pierini, Andrea, 2018. "Combining official and Google Trends data to forecast the Italian youth unemployment rate," Technological Forecasting and Social Change, Elsevier, vol. 130(C), pages 114-122.
  122. Azusa Matsumoto & Kohei Matsumura & Noriyuki Shiraki, 2013. "Potential of Search Data in Assessment of Current Economic Conditions," Bank of Japan Research Papers 2013-04-18, Bank of Japan.
  123. Simionescu, Mihaela & Zimmermann, Klaus F., 2017. "Big Data and Unemployment Analysis," GLO Discussion Paper Series 81, Global Labor Organization (GLO).
  124. Torsten Schmidt & Simeon Vosen, 2010. "A monthly consumption indicator for Germany based on internet search query data," Ruhr Economic Papers 0208, Rheinisch-Westfälisches Institut für Wirtschaftsforschung, Ruhr-Universität Bochum, Universität Dortmund, Universität Duisburg-Essen.
  125. Johannes Bock, 2018. "Quantifying macroeconomic expectations in stock markets using Google Trends," Papers 1805.00268, arXiv.org.
  126. Rubén Jesús Pérez-López & Jesús Everardo Olguín Tiznado & María Mojarro Magaña & Claudia Camargo Wilson & Juan Andrés López Barreras & Jorge Luis García-Alcaraz, 2019. "Information Sharing with ICT in Production Systems and Operational Performance," Sustainability, MDPI, Open Access Journal, vol. 11(13), pages 1-18, July.
  127. Cedric Mbanga & Ali F. Darrat & Jung Chul Park, 2019. "Investor sentiment and aggregate stock returns: the role of investor attention," Review of Quantitative Finance and Accounting, Springer, vol. 53(2), pages 397-428, August.
  128. Branislav Saxa, 2014. "Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand," Working Papers 2014/14, Czech National Bank.
  129. Vakrman, Tomas & Kristoufek, Ladislav, 2015. "Underpricing, underperformance and overreaction in initial pubic offerings: Evidence from investor attention using online searches," FinMaP-Working Papers 35, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
  130. Baumann, Alexendra & Wohlrabe, Klaus, 2019. "Publikationen von Wirtschaftsforschungsinstituten im deutschsprachigen Raum - Eine bibliometrische Analyse [Publications of Economic Research Insitutes in the German Speaking Area - A bibliometric ," MPRA Paper 92240, University Library of Munich, Germany.
  131. Tuhkuri, Joonas, 2016. "ETLAnow: A Model for Forecasting with Big Data – Forecasting Unemployment with Google Searches in Europe," ETLA Reports 54, The Research Institute of the Finnish Economy.
  132. David Iselin & Boriss Siliverstovs, 2013. "Mit Zeitungen Konjunkturprognosen erstellen: Eine Vergleichsstudie für die Schweiz und Deutschland," KOF Analysen, KOF Swiss Economic Institute, ETH Zurich, vol. 7(3), pages 104-117, September.
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  134. Lucia Kureková & Miroslav Beblavý & Anna Thum-Thysen, 2015. "Using online vacancies and web surveys to analyse the labour market: a methodological inquiry," IZA Journal of Labor Economics, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 4(1), pages 1-20, December.
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