The Impact of AI on Economic Modelling
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
- Stephen Hansen & Michael McMahon & Andrea Prat, 2018.
"Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach,"
The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 133(2), pages 801-870.
- Stephen Eliot Hansen & Michael McMahon & Andrea Prat, 2014. "Transparency and deliberation within the FOMC: A computational linguistics approach," Economics Working Papers 1425, Department of Economics and Business, Universitat Pompeu Fabra.
- Hansen, Stephen & McMahon, Michael & Prat, Andrea, 2014. "Transparency and deliberation within the FOMC: a computational linguistics approach," LSE Research Online Documents on Economics 58072, London School of Economics and Political Science, LSE Library.
- Andrea Prat & Michael McMahon & Stephen E. Hansen, 2015. "Transparency and Deliberation within the FOMC: a Computational Linguistics Approach," Working Papers 762, Barcelona School of Economics.
- Stephen Hansen & Michael McMahon & Andrea Prat, 2014. "Transparency and Deliberation within the FOMC: A Computational Linguistics Approach," CEP Discussion Papers dp1276, Centre for Economic Performance, LSE.
- Prat, Andrea & McMahon, Michael & Hansen, Stephen, 2014. "Transparency and Deliberation within the FOMC: a Computational Linguistics Approach," CEPR Discussion Papers 9994, C.E.P.R. Discussion Papers.
- Hansen, Stephen & McMahon, Michael & Prat, Andrea, 2014. "Transparency and deliberation within the FOMC: a computational linguistics approach," LSE Research Online Documents on Economics 60287, London School of Economics and Political Science, LSE Library.
- Stephen Hansen & Michael McMahon & Andrea Prat, 2014. "Transparency and Deliberation within the FOMC: a Computational Linguistics Approach," Discussion Papers 1411, Centre for Macroeconomics (CFM).
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020.
"Empirical Asset Pricing via Machine Learning,"
The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
- Shihao Gu & Bryan T. Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," Swiss Finance Institute Research Paper Series 18-71, Swiss Finance Institute.
- Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018. "Empirical Asset Pricing via Machine Learning," NBER Working Papers 25398, 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.
- Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1, May.
- El. Thalassinos & Th. Kiriazidis, 2003. "Degrees Of Integration In International Portfolio Diversification: Effective Systemic Risk," European Research Studies Journal, European Research Studies Journal, vol. 0(1-2), pages 119-130, January -.
- 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.
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.- 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.
- 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 Kotze & Neil Rankin & Daan Steenkamp, 2022. "Big data forecasting of South African inflation," ERSA Working Paper Series, Economic Research Southern Africa, vol. 0.
- 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.
- 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.
- 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.
- Celso Brunetti & Marc Joëts & Valérie Mignon, 2023.
"Reasons Behind Words: OPEC Narratives and the Oil Market,"
Working Papers
2023-19, CEPII research center.
- Celso Brunetti & Marc Joëts & Valérie Mignon, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers hal-04196053, HAL.
- Valérie Mignon & Celso Brunetti & Marc Joëts, 2023. "Reasons Behind Words: OPEC Narratives and the Oil Market," EconomiX Working Papers 2023-24, University of Paris Nanterre, EconomiX.
- Celso Brunetti & Marc Joëts & Valérie Mignon, 2024. "Reasons Behind Words: OPEC Narratives and the Oil Market," Finance and Economics Discussion Series 2024-003, Board of Governors of the Federal Reserve System (U.S.).
- Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
- Rubesam, Alexandre, 2022.
"Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market,"
Emerging Markets Review, Elsevier, vol. 51(PB).
- Alexandre Rubesam, 2022. "Machine learning portfolios with equal risk contributions: Evidence from the Brazilian market," Post-Print hal-03707365, HAL.
- Barua, Ronil & Sharma, Anil K., 2023. "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, vol. 58(PC).
- Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022. "On modeling IPO failure risk," Economic Modelling, Elsevier, vol. 109(C).
- Tom L. Dudda & Lars Hornuf, 2025. "The Perks and Perils of Machine Learning in Business and Economic Research," CESifo Working Paper Series 11721, CESifo.
- Paul Geertsema & Helen Lu, 2023. "Relative Valuation with Machine Learning," Journal of Accounting Research, Wiley Blackwell, vol. 61(1), pages 329-376, March.
- Vasilios Plakandaras & Ioannis Pragidis & Paris Karypidis, 2024. "Deciphering the U.S. metropolitan house price dynamics," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 52(2), pages 434-485, March.
- Pan, Shuiyang & Long, Suwan(Cheng) & Wang, Yiming & Xie, Ying, 2023. "Nonlinear asset pricing in Chinese stock market: A deep learning approach," International Review of Financial Analysis, Elsevier, vol. 87(C).
- Yulin Liu & Luyao Zhang, 2022. "Cryptocurrency Valuation: An Explainable AI Approach," Papers 2201.12893, arXiv.org, revised Jul 2023.
- Zhaoxing Gao & Ruey S. Tsay, 2023. "Supervised Dynamic PCA: Linear Dynamic Forecasting with Many Predictors," Papers 2307.07689, arXiv.org.
- Cheng, Louis T.W. & Cheong, Tsun Se & Wojewodzki, Michal & Chui, David, 2025. "The effect of ESG divergence on the financial performance of Hong Kong-listed firms: An artificial neural network approach," Research in International Business and Finance, Elsevier, vol. 73(PA).
- Wang, Nianling & Zhang, Mingzhi & Zhang, Yuan, 2024. "Return prediction: A tree-based conditional sort approach with firm characteristics," Finance Research Letters, Elsevier, vol. 60(C).
- Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
- 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.
- Branco, Rafael R. & Rubesam, Alexandre & Zevallos, Mauricio, 2024.
"Forecasting realized volatility: Does anything beat linear models?,"
Journal of Empirical Finance, Elsevier, vol. 78(C).
- Rafael Branco & Alexandre Rubesam & Mauricio Zevallos, 2024. "Forecasting realized volatility: Does anything beat linear models?," Post-Print hal-04835657, HAL.
- Philippe Goulet Coulombe, 2021. "To Bag is to Prune," Working Papers 21-03, Chair in macroeconomics and forecasting, University of Quebec in Montreal's School of Management, revised Jun 2021.
More about this item
Keywords
Artificial intelligence; modeling; econometrics; machine learning.;All these keywords.
JEL classification:
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C5 - Mathematical and Quantitative Methods - - Econometric Modeling
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
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:ers:journl:v:xxviii:y:2025:i:1:p:640-660. 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: Marios Agiomavritis (email available below). General contact details of provider: https://ersj.eu/ .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.