Machine learning, artificial neural networks and social research
Author
Abstract
Suggested Citation
DOI: 10.1007/s11135-020-01037-y
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ziad Obermeyer, 2015. "Prediction Policy Problems," American Economic Review, American Economic Association, vol. 105(5), pages 491-495, May.
- Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
- 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.
- Daniel J. Hopkins & Gary King, 2010. "A Method of Automated Nonparametric Content Analysis for Social Science," American Journal of Political Science, John Wiley & Sons, vol. 54(1), pages 229-247, January.
- Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Mohamed-Amine Babay & Mustapha Adar & Ahmed Chebak & Mustapha Mabrouki, 2023. "Dynamics of Gas Generation in Porous Electrode Alkaline Electrolysis Cells: An Investigation and Optimization Using Machine Learning," Energies, MDPI, vol. 16(14), pages 1-21, July.
- Hüseyin Cüce & Duygu Özçelik, 2022. "Application of Machine Learning (ML) and Artificial Intelligence (AI)-Based Tools for Modelling and Enhancing Sustainable Optimization of the Classical/Photo-Fenton Processes for the Landfill Leachate," Sustainability, MDPI, vol. 14(18), pages 1-16, September.
- Wadim Strielkowski & Svetlana Zenchenko & Anna Tarasova & Yana Radyukova, 2022. "Management of Smart and Sustainable Cities in the Post-COVID-19 Era: Lessons and Implications," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
- Miguel G. Folgado & Veronica Sanz, 2022. "Exploring the political pulse of a country using data science tools," Journal of Computational Social Science, Springer, vol. 5(1), pages 987-1000, May.
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.- Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
- 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).
- Filmer,Deon P. & Nahata,Vatsal & Sabarwal,Shwetlena, 2021. "Preparation, Practice, and Beliefs : A Machine Learning Approach to Understanding Teacher Effectiveness," Policy Research Working Paper Series 9847, The World Bank.
- Francesco Decarolis & Cristina Giorgiantonio, 2020. "Corruption red flags in public procurement: new evidence from Italian calls for tenders," Questioni di Economia e Finanza (Occasional Papers) 544, Bank of Italy, Economic Research and International Relations Area.
- Emanuel Kohlscheen, 2022.
"Quantifying the Role of Interest Rates, the Dollar and Covid in Oil Prices,"
Papers
2208.14254, arXiv.org, revised Oct 2022.
- Emanuel Kohlscheen, 2022. "Quantifying the role of interest rates, the Dollar and Covid in oil prices," BIS Working Papers 1040, Bank for International Settlements.
- Alessandra Garbero & Marco Letta, 2022. "Predicting household resilience with machine learning: preliminary cross-country tests," Empirical Economics, Springer, vol. 63(4), pages 2057-2070, October.
- Cerqua, Augusto & Letta, Marco, 2022.
"Local inequalities of the COVID-19 crisis,"
Regional Science and Urban Economics, Elsevier, vol. 92(C).
- Cerqua, Augusto & Letta, Marco, 2021. "Local inequalities of the COVID-19 crisis," GLO Discussion Paper Series 875, Global Labor Organization (GLO).
- Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
- Potnuru Kishen Suraj & Ankesh Gupta & Makkunda Sharma & Sourabh Bikas Paul & Subhashis Banerjee, 2017. "On monitoring development indicators using high resolution satellite images," Papers 1712.02282, arXiv.org, revised Jun 2018.
- Andini, Monica & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Salvestrini, Viola, 2018. "Targeting with machine learning: An application to a tax rebate program in Italy," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 86-102.
- Sophie-Charlotte Klose & Johannes Lederer, 2020. "A Pipeline for Variable Selection and False Discovery Rate Control With an Application in Labor Economics," Papers 2006.12296, arXiv.org, revised Jun 2020.
- de Lucio, Juan, 2021. "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
- Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
- McKenzie, David & Sansone, Dario, 2017.
"Man vs. Machine in Predicting Successful Entrepreneurs: Evidence from a Business Plan Competition in Nigeria,"
CEPR Discussion Papers
12523, C.E.P.R. Discussion Papers.
- Mckenzie,David J. & Sansone,Dario & Mckenzie,David J. & Sansone,Dario, 2017. "Man vs. machine in predicting successful entrepreneurs : evidence from a business plan competition in Nigeria," Policy Research Working Paper Series 8271, The World Bank.
- Battiston, Pietro & Gamba, Simona & Santoro, Alessandro, 2024. "Machine learning and the optimization of prediction-based policies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
- Andini, Monica & Boldrini, Michela & Ciani, Emanuele & de Blasio, Guido & D'Ignazio, Alessio & Paladini, Andrea, 2022.
"Machine learning in the service of policy targeting: The case of public credit guarantees,"
Journal of Economic Behavior & Organization, Elsevier, vol. 198(C), pages 434-475.
- Monica Andini & Michela Boldrini & Emanuele Ciani & Guido de Blasio & Alessio D'Ignazio & Andrea Paladini, 2019. "Machine learning in the service of policy targeting: the case of public credit guarantees," Temi di discussione (Economic working papers) 1206, Bank of Italy, Economic Research and International Relations Area.
- Max Vilgalys, 2023. "A Machine Learning Approach to Measuring Climate Adaptation," Papers 2302.01236, arXiv.org.
- de Blasio, Guido & D'Ignazio, Alessio & Letta, Marco, 2022. "Gotham city. Predicting ‘corrupted’ municipalities with machine learning," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
- Fabio Pammolli & Paolo Bonaretti & Massimo Riccaboni & Valentina Tortolini, 2019. "Quali Regole per la Spesa Farmaceutica? - Criticità, Impatti, Proposte," Working Papers CERM 01-2019, Competitività, Regole, Mercati (CERM).
- McKenzie, David & Sansone, Dario, 2019. "Predicting entrepreneurial success is hard: Evidence from a business plan competition in Nigeria," Journal of Development Economics, Elsevier, vol. 141(C).
More about this item
Keywords
Machine learning; Deep learning Artificial neural network; Supervised learning; Linear models; Nonlinear models;All these keywords.
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:spr:qualqt:v:55:y:2021:i:3:d:10.1007_s11135-020-01037-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.