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

Author

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  • 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
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