Regional economic integration and machine learning: Policy insights from the review of literature
Author
Abstract
Suggested Citation
DOI: 10.1016/j.jpolmod.2023.07.001
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
- Dominik Naeher, 2015. "An Empirical Estimation of Asia's Untapped Regional Integration Potential Using Data Envelopment Analysis," Asian Development Review, MIT Press, vol. 32(2), pages 178-195, September.
- Athey, Susan & Imbens, Guido W., 2019.
"Machine Learning Methods Economists Should Know About,"
Research Papers
3776, Stanford University, Graduate School of Business.
- Susan Athey & Guido Imbens, 2019. "Machine Learning Methods Economists Should Know About," Papers 1903.10075, arXiv.org.
- Saad Chiekh Ahmed Abi El Maaly, 2022. "What the Analysis of 136 Studies from 1960 to 2020 Tells Us About Comparative Regionalism Studies," Post-Print halshs-03918624, HAL.
- Dominik Naeher & Raghavan Narayanan, 2020. "Untapped regional integration potential: A global frontier analysis," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 29(6), pages 722-747, August.
- Haas, Ernst B., 1970. "The Study of Regional Integration: Reflections on the Joy and Anguish of Pretheorizing," International Organization, Cambridge University Press, vol. 24(4), pages 606-646, October.
- Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
- Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
- 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.
- Naeher, Dominik, 2015. "An Empirical Estimation of Asia's Untapped Regional Integration Potential Using Data Envelopment Analysis," ADB Economics Working Paper Series 445, Asian Development Bank.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Dominik Naeher & Philippe Lombaerde & Takfarinas Saber, 2025. "Evaluating accession decisions in customs unions: a dynamic machine learning approach," International Economics and Economic Policy, Springer, vol. 22(1), pages 1-27, February.
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.- James T. E. Chapman & Ajit Desai, 2023.
"Macroeconomic Predictions Using Payments Data and Machine Learning,"
Forecasting, MDPI, vol. 5(4), pages 1-32, November.
- James T. E. Chapman & Ajit Desai, 2022. "Macroeconomic Predictions using Payments Data and Machine Learning," Papers 2209.00948, arXiv.org.
- James Chapman & Ajit Desai, 2022. "Macroeconomic Predictions Using Payments Data and Machine Learning," Staff Working Papers 22-10, Bank of Canada.
- Byron Botha & Rulof Burger & Kevin Kotzé & Neil Rankin & Daan Steenkamp, 2023.
"Big data forecasting of South African inflation,"
Empirical Economics, Springer, vol. 65(1), pages 149-188, July.
- Byron Botha & Rulof Burger & Kevin Kotz & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," Working Papers 11022, South African Reserve Bank.
- Byron Botha & Kevin Kotze & Neil Rankin & Rulof P. Burger, 2022. "Big data forecasting of South African inflation," Working Papers 873, Economic Research Southern Africa.
- Byron Botha & Rulof Burger & Kevin Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," School of Economics Macroeconomic Discussion Paper Series 2022-03, School of Economics, University of Cape Town.
- Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020.
"Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds,"
LEO Working Papers / DR LEO
2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
- Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021. "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers hal-02507499, HAL.
- John Aoga & Juhee Bae & Stefanija Veljanoska & Siegfried Nijssen & Pierre Schaus, 2020. "Impact of weather factors on migration intention using machine learning algorithms," Papers 2012.02794, arXiv.org.
- Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.
- Zhang, Qin & Ni, He & Xu, Hao, 2023. "Nowcasting Chinese GDP in a data-rich environment: Lessons from machine learning algorithms," Economic Modelling, Elsevier, vol. 122(C).
- Falco J. Bargagli-Stoffi & Jan Niederreiter & Massimo Riccaboni, 2020. "Supervised learning for the prediction of firm dynamics," Papers 2009.06413, arXiv.org.
- Juhee Bae & John Aoga & Stefanija Veljanoska & Siegfried Nijssen & Pierre Schaus, 2020. "Impact of Weather Factors on Migration Intention using Machine Learning Algorithms," LIDAM Discussion Papers IRES 2020034, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
- Michael Pollmann, 2020. "Causal Inference for Spatial Treatments," Papers 2011.00373, arXiv.org, revised Jan 2023.
- 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.
- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022.
"Urban economics in a historical perspective: Recovering data with machine learning,"
Regional Science and Urban Economics, Elsevier, vol. 94(C).
- Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020. "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers 15308, C.E.P.R. Discussion Papers.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint) halshs-03673240, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print halshs-03673240, HAL.
- Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers 14392, Institute of Labor Economics (IZA).
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021. "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers halshs-03231786, HAL.
- Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022. "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," SciencePo Working papers Main halshs-03673240, HAL.
- Arenas, Andreu & Calsamiglia, Caterina, 2022.
"Gender Differences in High-Stakes Performance and College Admission Policies,"
IZA Discussion Papers
15550, Institute of Labor Economics (IZA).
- Andreu Arenas & Caterina Calsamiglia, 2023. "Gender Differences in High-Stakes Performance and College Admission Policies," Working Papers 2023/13, Institut d'Economia de Barcelona (IEB).
- 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.
- Dang,Hai-Anh H. & Kilic,Talip & Carletto,Calogero & Abanokova,Kseniya, 2021.
"Poverty Imputation in Contexts without Consumption Data : A Revisit with Further Refinements,"
Policy Research Working Paper Series
9838, The World Bank.
- Dang, Hai-Anh H & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2023. "Poverty Imputation in Contexts without Consumption Data: A Revisit with Further Refinements," IZA Discussion Papers 15873, Institute of Labor Economics (IZA).
- Dang, Hai-Anh H. & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2023. "Poverty Imputation in Contexts without Consumption Data: A Revisit with Further Refinements," GLO Discussion Paper Series 1226, Global Labor Organization (GLO).
- Dang, Hai-Anh H. & Kilic, Talip & Abanokova, Kseniya & Carletto, Calogero, 2024. "Poverty imputation in contexts without consumption data: a revisit with further refinements," LSE Research Online Documents on Economics 125798, London School of Economics and Political Science, LSE Library.
- Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
- Blankenship, Brian & Aklin, Michaël & Urpelainen, Johannes & Nandan, Vagisha, 2022. "Jobs for a just transition: Evidence on coal job preferences from India," Energy Policy, Elsevier, vol. 165(C).
- Andrei Dubovik & Adam Elbourne & Bram Hendriks & Mark Kattenberg, 2022. "Forecasting World Trade Using Big Data and Machine Learning Techniques," CPB Discussion Paper 441, CPB Netherlands Bureau for Economic Policy Analysis.
- Donna B. Gilleskie, 2021. "In sickness and in health, until death do us part: A case for theory," Southern Economic Journal, John Wiley & Sons, vol. 87(3), pages 753-768, January.
- Askitas, Nikos, 2024.
"A Hands-on Machine Learning Primer for Social Scientists: Math, Algorithms and Code,"
IZA Discussion Papers
17014, Institute of Labor Economics (IZA).
- Nikos Askitas & Nikolaos Askitas, 2024. "A Hands-On Machine Learning Primer for Social Scientists: Math, Algorithms and Code," CESifo Working Paper Series 11353, CESifo.
- Arthur Charpentier & Romuald Élie & Carl Remlinger, 2023. "Reinforcement Learning in Economics and Finance," Computational Economics, Springer;Society for Computational Economics, vol. 62(1), pages 425-462, June.
More about this item
Keywords
Regional economic integration; International trade; Machine learning; Artificial intelligence; Literature review;All these keywords.
JEL classification:
- C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
- C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
- F02 - International Economics - - General - - - International Economic Order and Integration
- F15 - International Economics - - Trade - - - Economic Integration
- F60 - International Economics - - Economic Impacts of Globalization - - - General
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:eee:jpolmo:v:45:y:2023:i:5:p:1077-1097. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505735 .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.