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

Author

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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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    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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