In Search of a Job: Forecasting Employment Growth Using Google Trends
Author
Abstract
Suggested Citation
DOI: 10.1080/07350015.2020.1791133
Download full text from publisher
As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.
Other versions of this item:
- 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.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Laurent Ferrara & Anna Simoni, 2023.
"When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 41(4), pages 1188-1202, October.
- Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working papers 717, Banque de France.
- Laurent Ferrara & Anna Simoni, 2023. "When are Google Data Useful to Nowcast GDP? An Approach via Preselection and Shrinkage," Post-Print hal-03919944, HAL.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," EconomiX Working Papers 2020-11, University of Paris Nanterre, EconomiX.
- Laurent Ferrara & Anna Simoni, 2019. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers 2019-04, Center for Research in Economics and Statistics.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Papers 2007.00273, arXiv.org, revised Sep 2022.
- Laurent Ferrara & Anna Simoni, 2020. "When are Google data useful to nowcast GDP? An approach via pre-selection and shrinkage," Working Papers hal-04159714, HAL.
- Borup, Daniel & Christensen, Bent Jesper & Mühlbach, Nicolaj Søndergaard & Nielsen, Mikkel Slot, 2023.
"Targeting predictors in random forest regression,"
International Journal of Forecasting, Elsevier, vol. 39(2), pages 841-868.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N{o}rgaard Muhlbach & Mikkel Slot Nielsen, 2020. "Targeting predictors in random forest regression," Papers 2004.01411, arXiv.org, revised Nov 2020.
- Daniel Borup & Bent Jesper Christensen & Nicolaj N. Mühlbach & Mikkel S. Nielsen, 2020. "Targeting predictors in random forest regression," CREATES Research Papers 2020-03, Department of Economics and Business Economics, Aarhus University.
- Diogo de Prince & Emerson Fernandes Marçal & Pedro L. Valls Pereira, 2022. "Forecasting Industrial Production Using Its Aggregated and Disaggregated Series or a Combination of Both: Evidence from One Emerging Market Economy," Econometrics, MDPI, vol. 10(2), pages 1-34, June.
- Bantis, Evripidis & Clements, Michael P. & Urquhart, Andrew, 2023. "Forecasting GDP growth rates in the United States and Brazil using Google Trends," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1909-1924.
- Borup, Daniel & Rapach, David E. & Schütte, Erik Christian Montes, 2023. "Mixed-frequency machine learning: Nowcasting and backcasting weekly initial claims with daily internet search volume data," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1122-1144.
- Kerkemeier, Marco & Kruse-Becher, Robinson, 2022. "Join the club! Dynamics of global ESG indices convergence," Finance Research Letters, Elsevier, vol. 49(C).
More about this item
JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
- E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
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:taf:jnlbes:v:40:y:2022:i:1:p:186-200. 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.
We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UBES20 .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.