IDEAS home Printed from https://ideas.repec.org/a/bba/j00007/v2y2023i2p35-47d242.html
   My bibliography  Save this article

Progress, Evolving Paradigms and Recent Trends in Economic Analysis

Author

Listed:
  • Robertas Damasevicius

    (Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania)

Abstract

This paper provides a thorough review of the shifting landscape of economic analysis, spotlighting recent trends and predicting future paths. While traditional economic models remain key for interpreting economic activity, they are being supplemented by fresh methods and cross-disciplinary viewpoints. The increased attention to inequality studies, using advanced statistical techniques and unique data sources, underscores the growing emphasis on fairness and distribution within economic analysis. The incorporation of behavioral elements into economic models also expands our comprehension of economic decision-making and market results. Notably, the emergence of computational economics-integrating artificial intelligence (AI), big data, and machine learning into economic scrutiny-represents a major development. Often referred to as ’smart economics,’ this field employs technology to formulate, address complex economic dilemmas, and perceive economic activity in unconventional ways. Yet, the application of AI and machine learning in economics introduces new hurdles around data privacy, algorithmic bias, and the transparency of model outcomes. The impact of the digital revolution on economic analysis is significant, as the advent of computational economics and the surge of big data are transforming research techniques and policy implications. Concurrently, the advent of the circular economy indicates a radical shift in our perspective on economic sustainability, carrying considerable implications for environmental policy and business tactics. In the future, it’s anticipated that these trends will further modify the realm of economic analysis, with AI and machine learning integration, emphasis on sustainability and fairness, and the influence of big data becoming more pronounced. As these changes take place, it’s imperative for researchers, policymakers, and practitioners to remain adaptable and flexible, prepared to capitalize on the opportunities and tackle the challenges these trends present.

Suggested Citation

  • Robertas Damasevicius, 2023. "Progress, Evolving Paradigms and Recent Trends in Economic Analysis," Financial Economics Letters, Anser Press, vol. 2(2), pages 35-47, October.
  • Handle: RePEc:bba:j00007:v:2:y:2023:i:2:p:35-47:d:242
    as

    Download full text from publisher

    File URL: https://www.anserpress.org/journal/fel/2/2/14/pdf
    Download Restriction: no

    File URL: https://www.anserpress.org/journal/fel/2/2/14
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Augusto Bianchini & Jessica Rossi & Marco Pellegrini, 2019. "Overcoming the Main Barriers of Circular Economy Implementation through a New Visualization Tool for Circular Business Models," Sustainability, MDPI, vol. 11(23), pages 1-33, November.
    2. Costello, Anna M. & Down, Andrea K. & Mehta, Mihir N., 2020. "Machine + man: A field experiment on the role of discretion in augmenting AI-based lending models," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    3. Ivan Deviatkin & Sanna Rousu & Malahat Ghoreishi & Mohammad Naji Nassajfar & Mika Horttanainen & Ville Leminen, 2022. "Implementation of Circular Economy Strategies within the Electronics Sector: Insights from Finnish Companies," Sustainability, MDPI, vol. 14(6), pages 1-11, March.
    4. 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.
    5. repec:hal:spmain:info:hdl:2441/4vsqk7docb9nmophtp29pk68cr is not listed on IDEAS
    6. Emmanuel Saez & Gabriel Zucman, 2016. "Editor's Choice Wealth Inequality in the United States since 1913: Evidence from Capitalized Income Tax Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(2), pages 519-578.
    7. Sergio Luis Nañez Alonso & Ricardo Francisco Reier Forradellas & Oriol Pi Morell & Javier Jorge-Vazquez, 2021. "Digitalization, Circular Economy and Environmental Sustainability: The Application of Artificial Intelligence in the Efficient Self-Management of Waste," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    8. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    9. Niu, Xiaoqin & Yüksel, Serhat & Dinçer, Hasan, 2023. "Emission strategy selection for the circular economy-based production investments with the enhanced decision support system," Energy, Elsevier, vol. 274(C).
    10. Okewu Emmanuel & Ananya M & Sanjay Misra & Murat Koyuncu, 2020. "A Deep Neural Network-Based Advisory Framework for Attainment of Sustainable Development Goals 1-6," Sustainability, MDPI, vol. 12(24), pages 1-16, December.
    11. Thomas Piketty & Emmanuel Saez, 2003. "Income Inequality in the United States, 1913–1998," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(1), pages 1-41.
    12. John M. Abowd & Ian M. Schmutte, 2019. "An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices," American Economic Review, American Economic Association, vol. 109(1), pages 171-202, January.
    13. Emmanuel Okewu & Sanjay Misra & Jonathan Okewu & Robertas Damaševičius & Rytis Maskeliūnas, 2019. "An Intelligent Advisory System to Support Managerial Decisions for A Social Safety Net," Administrative Sciences, MDPI, vol. 9(3), pages 1-14, August.
    14. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    15. Bekaert, Geert & De Santis, Roberto A., 2021. "Risk and return in international corporate bond markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).
    16. Raj Chetty & Nathaniel Hendren & Patrick Kline & Emmanuel Saez, 2014. "Where is the land of Opportunity? The Geography of Intergenerational Mobility in the United States," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(4), pages 1553-1623.
    17. Emmanuel Okewu & Sanjay Misra & Rytis Maskeliūnas & Robertas Damaševičius & Luis Fernandez-Sanz, 2017. "Optimizing Green Computing Awareness for Environmental Sustainability and Economic Security as a Stochastic Optimization Problem," Sustainability, MDPI, vol. 9(10), pages 1-17, October.
    18. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    19. Raj Chetty & John N. Friedman & Nathaniel Hendren & Maggie R. Jones & Sonya R. Porter, 2018. "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility," NBER Working Papers 25147, National Bureau of Economic Research, Inc.
    20. Stefano DellaVigna, 2018. "Structural Behavioral Economics," NBER Working Papers 24797, National Bureau of Economic Research, Inc.
    21. 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.
    Full references (including those not matched with items on IDEAS)

    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.
    1. Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
    2. Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
    3. Andreas Fagereng & Luigi Guiso & Davide Malacrino & Luigi Pistaferri, 2020. "Heterogeneity and Persistence in Returns to Wealth," Econometrica, Econometric Society, vol. 88(1), pages 115-170, January.
    4. 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.
    5. Akash Malhotra, 2018. "A hybrid econometric-machine learning approach for relative importance analysis: Prioritizing food policy," Papers 1806.04517, arXiv.org, revised Aug 2020.
    6. Atif Mian & Ludwig Straub & Amir Sufi, 2021. "Indebted Demand," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(4), pages 2243-2307.
    7. Kea BARET, 2021. "Fiscal rules’ compliance and Social Welfare," Working Papers of BETA 2021-38, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    8. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    9. Galdo, Virgilio & Li, Yue & Rama, Martin, 2021. "Identifying urban areas by combining human judgment and machine learning: An application to India," Journal of Urban Economics, Elsevier, vol. 125(C).
    10. Kreiner, Claus Thustrup & Olufsen, Isabel Skak, 2022. "Is inequality in subjective well-being meritocratic? Danish evidence from linked survey and administrative data," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 336-367.
    11. Pablo Picardo, 2019. "Predicción de precios de vivienda: Aprendizaje estadístico con datos de oferta y transacciones para la ciudad de Montevideo," Documentos de trabajo 2019002, Banco Central del Uruguay.
    12. Mario Alloza, 2021. "The impact of taxes on income mobility," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 28(4), pages 794-854, August.
    13. Lucas Chancel, 2019. "Ten facts about income inequality in advanced economies," Working Papers hal-02876982, HAL.
    14. Joey Blumberg & Gary Thompson, 2022. "Nonparametric segmentation methods: Applications of unsupervised machine learning and revealed preference," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(3), pages 976-998, May.
    15. 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.
    16. Francesco Bloise & Paolo Brunori & Patrizio Piraino, 2021. "Estimating intergenerational income mobility on sub-optimal data: a machine learning approach," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 19(4), pages 643-665, December.
    17. Mehmet Güney Celbiş, 2021. "A machine learning approach to rural entrepreneurship," Papers in Regional Science, Wiley Blackwell, vol. 100(4), pages 1079-1104, August.
    18. James T. E. Chapman & Ajit Desai, 2023. "Macroeconomic Predictions Using Payments Data and Machine Learning," Forecasting, MDPI, vol. 5(4), pages 1-32, November.
    19. Onder Ozgur & Erdal Tanas Karagol & Fatih Cemil Ozbugday, 2021. "Machine learning approach to drivers of bank lending: evidence from an emerging economy," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-29, December.
    20. 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.

    Corrections

    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:bba:j00007:v:2:y:2023:i:2:p:35-47:d:242. 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: Ramona Wang (email available below). General contact details of provider: https://www.anserpress.org .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.