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Advanced Econometric Models In The Digital Age: Bibliometric Analysis

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  • Anamaria-Geanina MACOVEI

    (Stefan cel Mare University of Suceava, 720229, Romania)

Abstract

This research highlights the importance of econometrics and advanced econometric models in the analysis and forecasting of complex economic phenomena. Econometrics is presented as a fundamental interdisciplinary discipline that integrates statistical and mathematical methods into economic theory and provides a rigorous framework for understanding economic dynamics. Based on the Web of Science database (1975-2024), bibliometric analysis using the VOSviewer programme was used to identify the main research directions, authors and countries that have made significant contributions in this field. The results revealed key trends, including increased interest in "multiple regression", "production function (Cobb-Douglas)", "Logit", "ARIMA", "SARIMA", "ARCH", "GARCH", "SVAR" models, as well as the integration of artificial intelligence methods into econometric analysis. The conclusion highlights the crucial role of econometric models in supporting economic decisions and making reliable forecasts, demonstrating the dynamic and adaptable nature of contemporary econometrics.

Suggested Citation

  • Anamaria-Geanina MACOVEI, 2025. "Advanced Econometric Models In The Digital Age: Bibliometric Analysis," European Journal of Accounting, Finance & Business, "Stefan cel Mare" University of Suceava, Romania - Faculty of Economics and Public Administration, West University of Timisoara, Romania - Faculty of Economics and Business Administration, vol. 13(2), pages 71-80, June.
    • Handle: RePEc:scm:ejafbu:v:13:y:2025:i:2:p:71-80
    DOI: 10.4316/EJAFB.2025.13208
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    References listed on IDEAS

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    1. Manabu Asai & Michael McAleer, 2022. "Bayesian Analysis of Realized Matrix-Exponential GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 103-123, January.
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Bjerkholt, Olav & Qin, Duo, 2010. "Teaching Economics as a Science: the 1930 Yale Lectures of Ragnar Frisch," Memorandum 05/2010, Oslo University, Department of Economics.
    4. Bouri, Elie & Gupta, Rangan & Pierdzioch, Christian & Polat, Onur, 2024. "Forecasting U.S. recessions using over 150 years of data: Stock-market moments versus oil-market moments," Finance Research Letters, Elsevier, vol. 69(PB).
    5. Bjerkholt, Olav & Qin, Duo, 2010. "Teaching Economics as a Science: the 1930 Yale Lectures of Ragnar Frisch," Memorandum 05/2010, Oslo University, Department of Economics.
    6. 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.
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