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Development of the Near-Term Forecast of Inflation for Uzbekistan: Application of FAVAR and BVAR models

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  • Temurbek Boymirzaev

    (Central Bank of Uzbekistan)

Abstract

This study investigates the application of Factor-Augmented Vector Autoregression (FAVAR) and Bayesian Vector Autoregression (BVAR) models for inflation forecasting. FAVAR models deal with high-dimensional data by extracting latent factors from extensive macroeconomic indicators, while BVAR models incorporate prior distributions to enhance forecast stability and precision in data-limited environments. Employing a comprehensive dataset of Uzbekistan-specific inflation determinants, we conduct an empirical assessment of both models, examining their predictive accuracy. Findings from this research aim to optimize inflation forecasting methodologies, providing the Central Bank of Uzbekistan with robust, data-driven insights for improved policy formulation.

Suggested Citation

  • Temurbek Boymirzaev, 2025. "Development of the Near-Term Forecast of Inflation for Uzbekistan: Application of FAVAR and BVAR models," IHEID Working Papers 06-2025, Economics Section, The Graduate Institute of International Studies.
    • Handle: RePEc:gii:giihei:heidwp06-2025
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    More about this item

    Keywords

    FAVAR; BVAR; inflation forecast; forecast combination;
    All these keywords.

    JEL classification:

    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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