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Forecasting inflation in Iran by applying machine learning algorithms to PPP lag

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  • Tal BOGER

Abstract

This study applies machine learning techniques to forecast inflation in Iran using purchasing power parity (PPP) lag variables. It compares the performance of various algorithms, including decision trees, support vector machines, and neural networks, in predicting inflation trends. The findings suggest that machine learning models can offer improved accuracy over traditional econometric approaches, especially in volatile economic environments.

Suggested Citation

  • Tal BOGER, 2025. "Forecasting inflation in Iran by applying machine learning algorithms to PPP lag," Turkish Economic Review, EconSciences Journals, vol. 12(2), pages 68-82, June.
    • Handle: RePEc:cvv:journ2:v:12:y:2025:i:2:p:68-82
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    References listed on IDEAS

    as
    1. 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.
    2. 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)

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    More about this item

    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • F31 - International Economics - - International Finance - - - Foreign Exchange

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