Analyzing the Role of Machine Learning Algorithms in Inflation Forecasting and Its Implications for Economic Policymaking
Keywords:
Machine learning, Inflation forecasting, Economic policymaking, Econometrics, Thematic analysis, Predictive algorithms, Data governanceAbstract
This study aimed to analyze the role of machine learning algorithms in improving inflation forecasting and to explain their implications for economic policymaking. This qualitative study was conducted using a thematic analysis design. The participants consisted of experts in macroeconomics, monetary policymaking, economic data analysis, econometrics, financial markets, and financial technology in Tehran. Participants were selected through purposive and snowball sampling, and data collection continued until theoretical saturation was reached. In total, 19 experts participated in semi-structured interviews. The interview guide focused on the predictive capacity of machine learning algorithms, limitations of traditional econometric models, data quality and availability, model interpretability, institutional requirements, and policy consequences of algorithmic inflation forecasts. All interviews were transcribed verbatim and analyzed using NVivo software. Data analysis was performed through open coding, development of subthemes, and abstraction of main categories. The analysis resulted in four main categories: “the capacity of machine learning algorithms to detect nonlinear relationships and hidden inflationary patterns,” “dependence of forecast accuracy on data quality, timeliness, and integration,” “interpretability, institutional trust, and policy accountability challenges,” and “the application of algorithmic forecasts in improving the timing, targeting, and coordination of economic policies.” Participants emphasized that algorithms such as random forests, gradient boosting, neural networks, and hybrid models can provide a more accurate picture of inflation dynamics in data-rich environments. However, they also argued that policy reliance on these models without methodological transparency, forecast-error evaluation, and explainability mechanisms may lead to mechanical decision-making and weakened public trust. The findings indicate that machine learning can serve as a powerful complement to traditional econometric models in inflation forecasting, but it should not replace expert judgment and institutional analysis. Effective use of these algorithms in economic policymaking requires reliable data infrastructure, algorithmic governance, model interpretability, policymaker training, and integration of quantitative forecasts with political-economic understanding of inflation.
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