Skills for the future – forecasting firm competitiveness using machine learning methods and employer–employee register data
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
- Brunello, Giorgio & Wruuck, Patricia & Maurin, Laurent, 2019. "Skill shortages and skill mismatch in Europe: A review of the literature," EIB Working Papers 2019/05, European Investment Bank (EIB).
- Ron Boschma & Rikard Eriksson & Urban Lindgren, 2009.
"How does labour mobility affect the performance of plants? The importance of relatedness and geographical proximity,"
Journal of Economic Geography, Oxford University Press, vol. 9(2), pages 169-190, March.
- Ron Boschma & R. Eriksson & U. Lindgren, 2008. "How does Labour Mobility affect the Performance of Plants? The importance of relatedness and geographical proximity," DRUID Working Papers 08-14, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
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.- Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
- Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
- Nils Grashof, 2020. "Sinking or swimming in the cluster labour pool? A firm-specific analysis of the effect of specialized labour," Jena Economics Research Papers 2020-006, Friedrich-Schiller-University Jena.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022.
"How is machine learning useful for macroeconomic forecasting?,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2019. "How is Machine Learning Useful for Macroeconomic Forecasting?," CIRANO Working Papers 2019s-22, CIRANO.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Working Papers 20-01, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Aug 2020.
- Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & St'ephane Surprenant, 2020. "How is Machine Learning Useful for Macroeconomic Forecasting?," Papers 2008.12477, arXiv.org.
- Thiemo Fetzer & Stephan Kyburz, 2018.
"Cohesive Institutions and Political Violence,"
HiCN Working Papers
271, Households in Conflict Network.
- Thiemo Fetzer & Stephan Kyburz, 2019. "Cohesive Institutions and Political Violence," Working Papers 503, Center for Global Development.
- Thiemo Fetzer & Stephan Kyburz, 2018. "Cohesive Institutions and Political Violence," OxCarre Working Papers 210, Oxford Centre for the Analysis of Resource Rich Economies, University of Oxford.
- Fetzer, Thiemo & Kyburz, Stephan, 2018. "Cohesive Institutions and Political Violence," CAGE Online Working Paper Series 377, Competitive Advantage in the Global Economy (CAGE).
- Thiemo Fetzer & Stephan Kyburz, 2018. "Cohesive Institutions and Political Violence," Empirical Studies of Conflict Project (ESOC) Working Papers 11, Empirical Studies of Conflict Project.
- Fetzer, Thiemo & Kyburz, Stephan, 2018. "Cohesive Institutions and Political Violence," The Warwick Economics Research Paper Series (TWERPS) 1166, University of Warwick, Department of Economics.
- Tobias Götze & Marc Gürtler & Eileen Witowski, 2020. "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 428-446, September.
- Marte C.W. Solheim & Ron Boschma & Sverre Herstad, 2018. "Related variety, unrelated variety and the novelty content of firm innovation in urban and non-urban locations," Papers in Evolutionary Economic Geography (PEEG) 1836, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Oct 2018.
- Sascha O. Becker & Thiemo Fetzer, 2018.
"Has Eastern European Migration Impacted UK-born Workers?,"
CAGE Online Working Paper Series
376, Competitive Advantage in the Global Economy (CAGE).
- Becker, Sascha O. & Fetzer, Thiemo, 2018. "Has Eastern European Migration Impacted UK-born Workers?," The Warwick Economics Research Paper Series (TWERPS) 1165, University of Warwick, Department of Economics.
- Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
- Ernest Miguélez & Rosina Moreno, 2013.
"Do Labour Mobility and Technological Collaborations Foster Geographical Knowledge Diffusion? The Case of European Regions,"
Growth and Change, Wiley Blackwell, vol. 44(2), pages 321-354, June.
- Ernest Miguélez & Rosina Moreno, 2013. "“Do labour mobility and technological collaborations foster geographical knowledge diffusion? The case of European regions”," IREA Working Papers 201314, University of Barcelona, Research Institute of Applied Economics, revised Jul 2013.
- Ernest Miguélez & Rosina Moreno, 2013. "“Do labour mobility and technological collaborations foster geographical knowledge diffusion? The case of European regions”," AQR Working Papers 201306, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2013.
- Ernest Miguélez & Rosina Moreno, 2013. "Do Labour Mobility and Technological Collaborations Foster Geographical Knowledge Diffusion? The Case of European Regions," Post-Print halshs-03910281, HAL.
- Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
- Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
- Ron Boschma & Ron Martin, 2010.
"The Aims and Scope of Evolutionary Economic Geography,"
Chapters, in: Ron Boschma & Ron Martin (ed.), The Handbook of Evolutionary Economic Geography, chapter 1,
Edward Elgar Publishing.
- Ron Boschma & Ron Martin, 2010. "The Aims and Scope of Evolutionary Economic Geography," Papers in Evolutionary Economic Geography (PEEG) 1001, Utrecht University, Department of Human Geography and Spatial Planning, Group Economic Geography, revised Jan 2010.
- Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
- Tsang, Andrew, 2021.
"Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy,"
MPRA Paper
110703, University Library of Munich, Germany.
- Tsang, Andrew, 2021. "Uncovering Heterogeneous Regional Impacts of Chinese Monetary Policy," WiSo-HH Working Paper Series 62, University of Hamburg, Faculty of Business, Economics and Social Sciences, WISO Research Laboratory.
- Suriyan Jomthanachai & Wai Peng Wong & Khai Wah Khaw, 2024. "An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 741-792, February.
- Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
- Kristoffer B. Birkeland & Allan D. D'Silva & Roland Füss & Are Oust, 2021. "The Predictability of House Prices: "Human Against Machine"," International Real Estate Review, Global Social Science Institute, vol. 24(2), pages 139-183.
- Lamperti, Francesco & Roventini, Andrea & Sani, Amir, 2018.
"Agent-based model calibration using machine learning surrogates,"
Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 366-389.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Working Papers hal-01499344, HAL.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Working Papers hal-03458875, HAL.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-01499344, HAL.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," SciencePo Working papers Main hal-01499344, HAL.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Sciences Po publications 2017-09, Sciences Po.
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," Papers 1703.10639, arXiv.org, revised Apr 2017.
- Frencesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-based model calibration using machine learning surrogates," Documents de Travail de l'OFCE 2017-09, Observatoire Francais des Conjonctures Economiques (OFCE).
- Francesco Lamperti & Andrea Roventini & Amir Sani, 2017. "Agent-Based Model Calibration using Machine Learning Surrogates," LEM Papers Series 2017/11, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
- Bryan T. Kelly & Asaf Manela & Alan Moreira, 2019. "Text Selection," NBER Working Papers 26517, National Bureau of Economic Research, Inc.
More about this item
Keywords
Lasso; Random Forest; Gradient Boosting; Skills; Education;All these keywords.
JEL classification:
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- L6 - Industrial Organization - - Industry Studies: Manufacturing
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:ebl:ecbull:eb-20-01191. 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: John P. Conley (email available below). General contact details of provider: .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.