IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i1p168-d1560797.html
   My bibliography  Save this article

Machine Learning vs. Econometric Models to Forecast Inflation Rate in Romania? The Role of Sentiment Analysis

Author

Listed:
  • Mihaela Simionescu

    (Faculty of Business and Administration, University of Bucharest, 4-12, Blvd. Regina Elisabeta, 030108 Bucharest, Romania
    Academy of Romanian Scientists, Ilfov 3, 050044 Bucharest, Romania
    Institute for Economic Forecasting, Romanian Academy, 13, Calea 13 Septembrie, 050711 Bucharest, Romania)

Abstract

Given the high inflationary pressure in Romania, the aim of this paper is to demonstrate the potential of autoregressive distributed lag (ARDL) models incorporating sentiment analysis to provide better inflation forecasts compared to machine learning (ML) techniques. Sentiment analysis based on National Bank of Romania reports on quarterly inflation may provide valuable inputs for econometric models. The ARDL model, utilizing inflation and sentiment index data from the previous period, outperformed the proposed seasonal autoregressive integrated moving average (SARIMA) model and the ML techniques (support vector machine and artificial neural networks). The forecasts based on the ARDL model predicted correctly all the changes in inflation, while accuracy measures (mean error, mean absolute error, root squared mean error) in the short-run 2023: Q1–2024: Q3 indicated the most accurate predictions. The more accurate forecasts are essential for national banks, companies, policymakers, and households.

Suggested Citation

  • Mihaela Simionescu, 2025. "Machine Learning vs. Econometric Models to Forecast Inflation Rate in Romania? The Role of Sentiment Analysis," Mathematics, MDPI, vol. 13(1), pages 1-18, January.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:1:p:168-:d:1560797
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/1/168/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/13/1/168/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chakraborty, Chiranjit & Joseph, Andreas, 2017. "Machine learning at central banks," Bank of England working papers 674, Bank of England.
    2. Sebastian Kripfganz & Daniel C. Schneider, 2023. "ardl: Estimating autoregressive distributed lag and equilibrium correction models," Stata Journal, StataCorp LP, vol. 23(4), pages 983-1019, December.
    3. Sebastian Kripfganz & Daniel C. Schneider, 2023. "ardl: Estimating autoregressive distributed lag and equilibrium correction models," Stata Journal, StataCorp LP, vol. 23(4), pages 983-1019, December.
    4. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2018. "Econometrics and Machine Learning," Economie et Statistique / Economics and Statistics, Institut National de la Statistique et des Etudes Economiques (INSEE), issue 505-506, pages 147-169.
    5. Akhter, Tahsina, 2013. "Short-Term Forecasting of Inflation in Bangladesh with Seasonal ARIMA Processes," MPRA Paper 43729, University Library of Munich, Germany.
    6. Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2019. "Forecasting with instabilities: An application to DSGE models with financial frictions," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    7. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    8. M. Ali Choudhary & Adnan Haider, 2012. "Neural network models for inflation forecasting: an appraisal," Applied Economics, Taylor & Francis Journals, vol. 44(20), pages 2631-2635, July.
    9. Michael Woodford, 2007. "The Case for Forecast Targeting as a Monetary Policy Strategy," Journal of Economic Perspectives, American Economic Association, vol. 21(4), pages 3-24, Fall.
    10. Mizrach, B, 1992. "Multivariate Nearest-Neighbor Forecasts of EMS Exchange Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages 151-163, Suppl. De.
    11. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
    12. Ivan Baybuza, 2018. "Inflation Forecasting Using Machine Learning Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 77(4), pages 42-59, December.
    13. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    14. 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.
    15. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
    16. McNees, Stephen K., 1990. "The role of judgment in macroeconomic forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 6(3), pages 287-299, October.
    17. Palar, Pramudita Satria & Zuhal, Lavi Rizki & Shimoyama, Koji, 2023. "Enhancing the explainability of regression-based polynomial chaos expansion by Shapley additive explanations," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    18. Gour Sundar Mitra Thakur & Rupak Bhattacharyya & Seema Sarkar Mondal, 2016. "Artificial Neural Network Based Model for Forecasting of Inflation in India," Fuzzy Information and Engineering, Taylor & Francis Journals, vol. 8(1), pages 87-100, March.
    19. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    20. Weijia (Vivian) Li & Kara M. Kockelman, 2022. "How does machine learning compare to conventional econometrics for transport data sets? A test of ML versus MLE," Growth and Change, Wiley Blackwell, vol. 53(1), pages 342-376, March.
    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. Nuno Carlos Leitão, 2021. "Testing the Role of Trade on Carbon Dioxide Emissions in Portugal," Economies, MDPI, vol. 9(1), pages 1-15, February.
    2. Boucekkine, R. & Laksaci, M. & Touati-Tliba, M., 2021. "Long-run stability of money demand and monetary policy: The case of Algeria," The Journal of Economic Asymmetries, Elsevier, vol. 24(C).
    3. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    4. Bouazza Elamine Zemri & Sidi Mohamed Boumediene Khetib, 2024. "Can Sustainable Economic Development Curtail Carbon Dioxide Emissions? Insights from Algeria’s Industry," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 70-83.
    5. Stéphane Lemarié & Valérie Orozco & Jean-Pierre Butault & Antonio Musolesi & Michel Simioni & Bertrand Schmitt, 2020. "Assessing the long-term impact of agricultural research on productivity: evidence from France," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(4), pages 1559-1586.
    6. Sebastian Kripfganz & Daniel C. Schneider, 2020. "Response Surface Regressions for Critical Value Bounds and Approximate p‐values in Equilibrium Correction Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 82(6), pages 1456-1481, December.
    7. José F. Perles & Martín Sevilla & Ana B. Ramón-Rodríguez & María Jesús Such & Patricia Aranda, 2024. "Carry-over effects of tourism on traditional activities," Tourism Economics, , vol. 30(5), pages 1237-1256, August.
    8. 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.
    9. Moutinho, Victor & Santos de Oliveira, Helena M. & Viana Espinosa de Oliveira, Henrique & Puime Guillén, Félix, 2023. "The augmented and integrative model of economic growth: Theoretical and empirical evidence from USA," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    10. Maria Karadima & Helen Louri, 2021. "Determinants of Non-Performing Loans in Greece: the intricate role of fiscal expansion," GreeSE – Hellenic Observatory Papers on Greece and Southeast Europe 160, Hellenic Observatory, LSE.
    11. Max Resende & Juliano Leal & João Simoni, 2021. "Electricity demand in the iron ore industry: Evidence from Brazil," Economics Bulletin, AccessEcon, vol. 41(3), pages 929-937.
    12. Desire Wade Atchike & Zhen-Yu Zhao & Geriletu Bao, 2020. "The Relationship between Electricity Consumption, Foreign Direct Investment and Economic Growth: Case of Benin," International Journal of Energy Economics and Policy, Econjournals, vol. 10(4), pages 507-515.
    13. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    14. Maria Karadima & Helen Louri, 2022. "Government debt accumulation and non-performing loans: An ARDL bounds testing approach," Economics and Business Letters, Oviedo University Press, vol. 11(4), pages 150-160.
    15. Olufunmilayo T. Afolayan & Henry Okodua & Hassan Oaikhenan & Oluwatoyin Matthew, 2020. "Carbon Emissions, Human Capital Investment and Economic Development in Nigeria," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 427-437.
    16. Somoye, Oluwatoyin Abidemi & Ozdeser, Huseyin & Seraj, Mehdi, 2022. "Modeling the determinants of renewable energy consumption in Nigeria: Evidence from Autoregressive Distributed Lagged in error correction approach," Renewable Energy, Elsevier, vol. 190(C), pages 606-616.
    17. Xiuqin Zhang & Xudong Shi & Yasir Khan & Majid Khan & Saba Naz & Taimoor Hassan & Chenchen Wu & Tahir Rahman, 2023. "The Impact of Energy Intensity, Energy Productivity and Natural Resource Rents on Carbon Emissions in Morocco," Sustainability, MDPI, vol. 15(8), pages 1-22, April.
    18. Sascha Keil, 2021. "The Challenging Estimation Of Trade Elasticities:Tackling The Inconclusive Eurozone Evidence," Chemnitz Economic Papers 042, Department of Economics, Chemnitz University of Technology, revised May 2021.
    19. Maxwell Chukwudi Udeagha & Nicholas Ngepah, 2022. "Dynamic ARDL Simulations Effects of Fiscal Decentralization, Green Technological Innovation, Trade Openness, and Institutional Quality on Environmental Sustainability: Evidence from South Africa," Sustainability, MDPI, vol. 14(16), pages 1-35, August.
    20. Dervis Kirikkaleli & Hasan Güngör & Tomiwa Sunday Adebayo, 2022. "Consumption‐based carbon emissions, renewable energy consumption, financial development and economic growth in Chile," Business Strategy and the Environment, Wiley Blackwell, vol. 31(3), pages 1123-1137, March.

    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:gam:jmathe:v:13:y:2025:i:1:p:168-:d:1560797. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.