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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. Gutiérrez, Antonio, 2023. "La brecha de género en el emprendimiento y la cultura emprendedora: Evidencia con Google Trends [Entrepreneurship gender gap and entrepreneurial culture: Evidence from Google Trends]," MPRA Paper 115876, University Library of Munich, Germany.
  2. 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.
  3. Chi, Tsung-Li & Liu, Hung-Tsen & Chang, Chia-Chien, 2023. "Hedging performance using google Trends–Evidence from the indian forex options market," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 107-123.
  4. 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 Etudes Economiques (INSEE), issue 505-506, pages 65-87.
  5. 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.
  6. 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.
  7. Zhongchen Song & Tom Coupé, 2023. "Predicting Chinese consumption series with Baidu," Journal of Chinese Economic and Business Studies, Taylor & Francis Journals, vol. 21(3), pages 429-463, July.
  8. Brodeur, Abel & Clark, Andrew E. & Fleche, Sarah & Powdthavee, Nattavudh, 2021. "COVID-19, lockdowns and well-being: Evidence from Google Trends," Journal of Public Economics, Elsevier, vol. 193(C).
  9. 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.
  10. Chien-jung Ting & Yi-Long Hsiao & Rui-jun Su, 2022. "Application of the Real-Time Tourism Data in Nowcasting the Service Consumption in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 12(4), pages 1-4.
  11. 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.
  12. Grzegorz Michal Bulczak, 2021. "Use of Google Trends to Predict the Real Estate Market: Evidence from the United Kingdom," International Real Estate Review, Global Social Science Institute, vol. 24(4), pages 613-631.
  13. repec:zbw:rwirep:0382 is not listed on IDEAS
  14. 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.
  15. Chun Li & Jianhua He & Xingwu Duan, 2020. "The Relationship Exploration between Public Migration Attention and Population Migration from a Perspective of Search Query," IJERPH, MDPI, vol. 17(7), pages 1-18, April.
  16. repec:spo:wpmain:info:hdl:2441/63csdfkqvu9nfanvuffe3qk8r6 is not listed on IDEAS
  17. Claveria, Oscar, 2019. "Forecasting the unemployment rate using the degree of agreement in consumer unemployment expectations," Journal for Labour Market Research, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], vol. 53(1), pages 1-3.
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
  26. 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.
  27. Monokroussos, George & Zhao, Yongchen, 2020. "Nowcasting in real time using popularity priors," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1173-1180.
  28. van der Wielen, Wouter & Barrios, Salvador, 2021. "Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU," Journal of Economics and Business, Elsevier, vol. 115(C).
  29. 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.
  30. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
  31. 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.
  32. David Kohns & Arnab Bhattacharjee, 2019. "Interpreting Big Data in the Macro Economy: A Bayesian Mixed Frequency Estimator," CEERP Working Paper Series 010, Centre for Energy Economics Research and Policy, Heriot-Watt University.
  33. Caprotti, Federico, 2016. "Defining a new sector in the green economy: Tracking the techno-cultural emergence of the cleantech sector, 1990–2010," Technology in Society, Elsevier, vol. 46(C), pages 80-89.
  34. Marta Crispino & Vincenzo Mariani, 2023. "A tool to nowcast tourist overnight stays with payment data and complementary indicators," Questioni di Economia e Finanza (Occasional Papers) 746, Bank of Italy, Economic Research and International Relations Area.
  35. 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.
  36. David Iselin & Boriss Siliverstovs, 2013. "Using Newspapers for Tracking the Business Cycle," KOF Working papers 13-337, KOF Swiss Economic Institute, ETH Zurich.
  37. Francis Rathinam & Sayak Khatua & Zeba Siddiqui & Manya Malik & Pallavi Duggal & Samantha Watson & Xavier Vollenweider, 2021. "Using big data for evaluating development outcomes: A systematic map," Campbell Systematic Reviews, John Wiley & Sons, vol. 17(3), September.
  38. 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.
  39. Bentzen, Jeanet Sinding, 2021. "In crisis, we pray: Religiosity and the COVID-19 pandemic," Journal of Economic Behavior & Organization, Elsevier, vol. 192(C), pages 541-583.
  40. 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.
  41. Sebastian Schmitz, 2019. "The Effects of Germany's Statutory Minimum Wage on Employment and Welfare Dependency," German Economic Review, Verein für Socialpolitik, vol. 20(3), pages 330-355, August.
  42. repec:diw:diwwpp:dp1036 is not listed on IDEAS
  43. 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.
  44. repec:zbw:rwirep:0155 is not listed on IDEAS
  45. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
  46. 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.
  47. 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).
  48. Blanchflower, David G. & Bryson, Alex, 2021. "The Economics of Walking About and Predicting Unemployment," GLO Discussion Paper Series 922, Global Labor Organization (GLO).
  49. 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.
  50. Jacques Bughin, 2015. "Google searches and twitter mood: nowcasting telecom sales performance," Netnomics, Springer, vol. 16(1), pages 87-105, August.
  51. 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.
  52. Karaman Örsal, Deniz Dilan, 2021. "Onlinedaten und Konsumentscheidungen: Voraussagen anhand von Daten aus Social Media und Suchmaschinen," Edition HWWI: Chapters, in: Straubhaar, Thomas (ed.), Neuvermessung der Datenökonomie, volume 6, pages 157-172, Hamburg Institute of International Economics (HWWI).
  53. 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).
  54. 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.
  55. 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.
  56. 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.
  57. 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.
  58. Correa, Alexander, 2021. "Prediciendo la llegada de turistas a Colombia a partir de los criterios de Google Trends," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 95, pages 105-134, July.
  59. 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.
  60. 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.
  61. Levent Bulut, 2015. "Google Trends and Forecasting Performance of Exchange Rate Models," IPEK Working Papers 1505, Ipek University, Department of Economics.
  62. 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.
  63. 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.
  64. 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.
  65. 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.
  66. Vosen, Simeon & Schmidt, Torsten, 2012. "A monthly consumption indicator for Germany based on Internet search query data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(7), pages 683-687.
  67. repec:hal:spmain:info:hdl:2441/5k53daedc2827oa91tfpuscvbn is not listed on IDEAS
  68. Daniel Borup & Erik Christian Montes Schütte, 2022. "In Search of a Job: Forecasting Employment Growth Using Google Trends," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 186-200, January.
  69. Pietro Giorgio Lovaglio & Mario Mezzanzanica & Emilio Colombo, 2020. "Comparing time series characteristics of official and web job vacancy data," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(1), pages 85-98, February.
  70. 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.
  71. 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.
  72. 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.
  73. 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.
  74. 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.
  75. 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.
  76. repec:hal:spmain:info:hdl:2441/63csdfkqvu9nfanvuffe3qk8r6 is not listed on IDEAS
  77. 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.
  78. 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.
  79. Tefft, Nathan, 2011. "Insights on unemployment, unemployment insurance, and mental health," Journal of Health Economics, Elsevier, vol. 30(2), pages 258-264, March.
  80. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
  81. Mirko Seithe & Lena Calahorrano, 2014. "Analysing Party Preferences Using Google Trends," CESifo Working Paper Series 4631, CESifo.
  82. 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.
  83. 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.
  84. Chiu, Peng-Chia & Teoh, Siew Hong & Zhang, Yinglei & Huang, Xuan, 2023. "Using Google searches of firm products to detect revenue management," Accounting, Organizations and Society, Elsevier, vol. 109(C).
  85. 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, vol. 12(3), pages 1-10, January.
  86. 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.
  87. 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.
  88. Cheng, Maoyong & Qu, Yang, 2020. "Does bank FinTech reduce credit risk? Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 63(C).
  89. Aleksandar Bradic, 2012. "The Role of Social Feedback in Financing of Technology Ventures," Papers 1301.2196, arXiv.org.
  90. Latoeiro, Pedro & Ramos, Sofía B. & Veiga, Helena, 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.
  91. Pirschel, Inske, 2016. "Forecasting euro area recessions in real-time," Kiel Working Papers 2020, Kiel Institute for the World Economy (IfW Kiel).
  92. Olivier Gergaud & Victor Ginsburgh, 2016. "Evaluating the Economic Effects of Cultural Events," Working Papers ECARES ECARES 2016-24, ULB -- Universite Libre de Bruxelles.
  93. 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.
  94. 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.
  95. Chien-jung Ting & Yi-Long Hsiao, 2022. "Nowcasting the GDP in Taiwan and the Real-Time Tourism Data," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 12(3), pages 1-2.
  96. 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.
  97. 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.
  98. 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.
  99. 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.
  100. Bańbura, Marta & Belousova, Irina & Bodnár, Katalin & Tóth, Máté Barnabás, 2023. "Nowcasting employment in the euro area," Working Paper Series 2815, European Central Bank.
  101. Huijian Han & Zhiming Li & Zongwei Li, 2023. "Using Machine Learning Methods to Predict Consumer Confidence from Search Engine Data," Sustainability, MDPI, vol. 15(4), pages 1-12, February.
  102. 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.
  103. Nuarpear 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.
  104. 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.
  105. Geng, Hongyan & Guo, Pin & Cheng, Maoyong, 2023. "The dark side of bank FinTech: Evidence from a transition economy," Economic Analysis and Policy, Elsevier, vol. 80(C), pages 1811-1830.
  106. Anastasiou, Dimitrios & Bragoudakis, Zacharias & Giannoulakis, Stelios, 2021. "Perceived vs actual financial crisis and bank credit standards: Is there any indication of self-fulfilling prophecy?," Research in International Business and Finance, Elsevier, vol. 58(C).
  107. repec:spo:wpmain:info:hdl:2441/5k53daedc2827oa91tfpuscvbn is not listed on IDEAS
  108. Grazia Biorci & Antonella Emina & Michelangelo Puliga & Lisa Sella & Gianna Vivaldo, 2016. "Tweet-tales: moods of socio-economic crisis?," Working Papers 04/2016, IMT School for Advanced Studies Lucca, revised Jul 2016.
  109. 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).
  110. 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.
  111. Jaroslav Pavlicek & Ladislav Kristoufek, 2014. "Can Google searches help nowcast and forecast unemployment rates in the Visegrad Group countries?," Papers 1408.6639, arXiv.org.
  112. 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.
  113. 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).
  114. 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.
  115. 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.
  116. Erik Christian Montes Schütte, 2018. "In Search of a Job: Forecasting Employment Growth in the US using Google Trends," CREATES Research Papers 2018-25, Department of Economics and Business Economics, Aarhus University.
  117. 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.
  118. Askitas, Nikos & Zimmermann, Klaus F., 2009. "Googlemetrie und Arbeitsmarkt in der Wirtschaftskrise," IZA Standpunkte 17, Institute of Labor Economics (IZA).
  119. 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.
  120. Serhan Cevik, 2022. "Where should we go? Internet searches and tourist arrivals," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4048-4057, October.
  121. Francesco, D'Amuri, 2009. "Predicting unemployment in short samples with internet job search query data," MPRA Paper 18403, University Library of Munich, Germany.
  122. Ahmed Shoukry Rashad, 2022. "The Power of Travel Search Data in Forecasting the Tourism Demand in Dubai," Forecasting, MDPI, vol. 4(3), pages 1-11, July.
  123. Jiawei Du, 2020. "A Research on Cross-sectional Return Dispersion and Volatility of US Stock Market during COVID-19," Papers 2007.11546, arXiv.org, revised Mar 2021.
  124. Havranek, Tomas & Zeynalov, Ayaz, 2018. "Forecasting Tourist Arrivals with Google Trends and Mixed Frequency Data," EconStor Preprints 187420, ZBW - Leibniz Information Centre for Economics.
  125. 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.
  126. Aaronson, Daniel & Brave, Scott A. & Butters, R. Andrew & Fogarty, Michael & Sacks, Daniel W. & Seo, Boyoung, 2022. "Forecasting unemployment insurance claims in realtime with Google Trends," International Journal of Forecasting, Elsevier, vol. 38(2), pages 567-581.
  127. repec:ipg:wpaper:24 is not listed on IDEAS
  128. Andreea Avramescu & Arkadiusz Wiśniowski, 2021. "Now-casting Romanian migration into the United Kingdom by using Google Search engine data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 45(40), pages 1219-1254.
  129. Pincheira, Pablo & Hernández, Ana María, 2019. "Forecasting Unemployment Rates with International Factors," MPRA Paper 97855, University Library of Munich, Germany.
  130. Anastasiou, Dimitrios & Drakos, Konstantinos, 2021. "European depositors’ behavior and crisis sentiment," Journal of Economic Behavior & Organization, Elsevier, vol. 184(C), pages 117-136.
  131. 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.
  132. Hantzsche, Arno, 2022. "Fiscal uncertainty and sovereign credit risk," European Economic Review, Elsevier, vol. 148(C).
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  158. Eli Arditi & Eldad Yechiam & Gal Zahavi, 2015. "Association between Stock Market Gains and Losses and Google Searches," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-12, October.
  159. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
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  165. 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.
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  168. 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.
  169. Kohns, David & Bhattacharjee, Arnab, 2023. "Nowcasting growth using Google Trends data: A Bayesian Structural Time Series model," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1384-1412.
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  171. Samvel S. Lazaryan & Nikita E. German, 2018. "Forecasting Current GDP Dynamics With Google Search Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 83-94, December.
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  174. Pietro Giorgio Lovaglio, 2022. "Do job vacancies variations anticipate employment variations by sector? Some preliminary evidence from Italy," LABOUR, CEIS, vol. 36(1), pages 71-93, March.
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  182. 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.
  183. Branislav Saxa, 2014. "Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand," Working Papers 2014/14, Czech National Bank.
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  199. Asgari, Mahdi & Nemati, Mehdi & Zheng, Yuqing, 2018. "Nowcasting Food Stock Movement using Food Safety Related Web Search Queries," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266323, Southern Agricultural Economics Association.
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  207. Rodrigo Mulero & Alfredo Garcia-Hiernaux, 2023. "Forecasting unemployment with Google Trends: age, gender and digital divide," Empirical Economics, Springer, vol. 65(2), pages 587-605, August.
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