Replicating "Predicting the present with Google trends" by Hyunyoung Choi and Hal Varian (The Economic Record, 2012)*
* This paper is a replication of an original studyAuthor
Abstract
Suggested Citation
DOI: 10.5018/economics-ejournal.ja.2018-34
Download full text from publisher
Other versions of this item:
- Coupé, Tom, 2017. "Replicating "Predicting the present with Google trends" by Hyunyoung Choi and Hal Varian (The Economic Record, 2012)," Economics Discussion Papers 2017-76, Kiel Institute for the World Economy (IfW Kiel).
References listed on IDEAS
- Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
- 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.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Daniel E. O'Leary, 2024. "Toward an extended framework of exhaust data for predictive analytics: An empirical approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
- Hofman, Jake M. & Goldstein, Daniel G. & Sen, Siddhartha & Poursabzi-Sangdeh, Forough & Allen, Jennifer & Dong, Ling Liang & Fried, Brenda & Gaur, Harpreet & Hoq, Adnan & Mbazor, Emeka & Moreira, Naom, 2021. "Expanding the scope of reproducibility research through data analysis replications," Organizational Behavior and Human Decision Processes, Elsevier, vol. 164(C), pages 192-202.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- 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.
- Götz, Thomas B. & Knetsch, Thomas A., 2017. "Google data in bridge equation models for German GDP," Discussion Papers 18/2017, Deutsche Bundesbank.
- 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.
- 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.
- Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel Sacks & Boyoung Seo, 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, revised 16 Apr 2020.
- Daniel Aaronson & Scott A. Brave & R. Andrew Butters & Daniel W. Sacks & Boyoung Seo, 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.
- Konstantinos N. Konstantakis & Despoina Paraskeuopoulou & Panayotis G. Michaelides & Efthymios G. Tsionas, 2021. "Bank deposits and Google searches in a crisis economy: Bayesian non‐linear evidence for Greece (2009–2015)," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 5408-5424, October.
- 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.
- Mioara, POPESCU, 2015. "Construction Of Economic Indicators Using Internet Searches," Annals of Spiru Haret University, Economic Series, Universitatea Spiru Haret, vol. 6(1), pages 25-31.
- Francesco Capozza & Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2021.
"Studying Information Acquisition in the Field: A Practical Guide and Review,"
CEBI working paper series
21-15, University of Copenhagen. Department of Economics. The Center for Economic Behavior and Inequality (CEBI).
- Francesco Capozza & Ingar Haaland & Christopher Roth & Johannes Wohlfart, 2021. "Studying Information Acquisition in the Field: A Practical Guide and Review," ECONtribute Discussion Papers Series 124, University of Bonn and University of Cologne, Germany.
- Tommaso Colussi & Ingo E. Isphording & Nico Pestel, 2021.
"Minority Salience and Political Extremism,"
American Economic Journal: Applied Economics, American Economic Association, vol. 13(3), pages 237-271, July.
- Colussi, Tommaso & Isphording, Ingo E. & Pestel, Nico, 2016. "Minority Salience and Political Extremism," IZA Discussion Papers 10417, Institute of Labor Economics (IZA).
- Tommaso Colussi & Ingo Isphording & Nico Pestel, 2019. "Minority Salience and Political Extremism," DISCE - Working Papers del Dipartimento di Economia e Finanza def080, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
- Kučerová, Zuzana & Pakši, Daniel & Koňařík, Vojtěch, 2024. "Macroeconomic fundamentals and attention: What drives european consumers’ inflation expectations?," Economic Systems, Elsevier, vol. 48(1).
- David W Carter & Scott Crosson & Christopher Liese, 2015. "Nowcasting Intraseasonal Recreational Fishing Harvest with Internet Search Volume," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-18, September.
- David H Chae & Sean Clouston & Mark L Hatzenbuehler & Michael R Kramer & Hannah L F Cooper & Sacoby M Wilson & Seth I Stephens-Davidowitz & Robert S Gold & Bruce G Link, 2015. "Association between an Internet-Based Measure of Area Racism and Black Mortality," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-12, April.
- C. Douglas Swearingen & Joseph T. Ripberger, 2014. "Google Insights and U.S. Senate Elections: Does Search Traffic Provide a Valid Measure of Public Attention to Political Candidates?," Social Science Quarterly, Southwestern Social Science Association, vol. 95(3), pages 882-893, September.
- 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.
- Max Nathan & Anna Rosso, 2014. "Mapping Information Economy Business with Big Data: Findings from the UK," National Institute of Economic and Social Research (NIESR) Discussion Papers 442, National Institute of Economic and Social Research.
- Anttonen, Jetro, 2018. "Nowcasting the Unemployment Rate in the EU with Seasonal BVAR and Google Search Data," ETLA Working Papers 62, The Research Institute of the Finnish Economy.
- Ishani Chaudhuri & Parthajit Kayal, 2022. "Predicting Power of Ticker Search Volume in Indian Stock Market," Working Papers 2022-214, Madras School of Economics,Chennai,India.
- 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.
- Sansone, Dario, 2019.
"Pink work: Same-sex marriage, employment and discrimination,"
Journal of Public Economics, Elsevier, vol. 180(C).
- Sansone, Dario, 2018. "Pink Work: Same-Sex Marriage, Employment and Discrimination," MPRA Paper 87998, University Library of Munich, Germany.
- Dario Sansone, 2018. "Pink Work: Same-Sex Marriage, Employment and Discrimination," Papers 1807.06698, arXiv.org.
- Dario Sansone, 2018. "“Pink Work. Same-Sex Marriage, Employment and Discrimination"," CeRP Working Papers 184, Center for Research on Pensions and Welfare Policies, Turin (Italy).
- Dario Sansone, 2018. "Pink Work: Same-Sex Marriage, Employment and Discrimination," 2018 Papers psa1336, Job Market Papers.
- Pulkit Sharma & Achut Manandhar & Patrick Thomson & Jacob Katuva & Robert Hope & David A. Clifton, 2019. "Combining Multi-Modal Statistics for Welfare Prediction Using Deep Learning," Sustainability, MDPI, vol. 11(22), pages 1-15, November.
- John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
Replication
This item is a replication of:More about this item
Keywords
Replication;JEL classification:
- A1 - General Economics and Teaching - - General Economics
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:ifweej:201834. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/iwkiede.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.