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A Suite of Models for CPI Forecasting

Author

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  • Nadiia Shapovalenko

    (National Bank of Ukraine)

Abstract

This paper reviews the suite of models the National Bank of Ukraine uses for short-term forecasting of CPI components. I examine the forecasting accuracy of the following econometric models: univariate models, VAR, FAVAR, Bayesian VAR models, and Error Correction models. The findings suggest that for almost all components there are models that outperform benchmark AR models. However, the best performing individual model at each horizon for each component differs. Combined forecasts obtained by averaging the models’ forecasts produce acceptable and robust results. Specifically, the combined forecasts are most accurate for core inflation, while they can beat the AR benchmark more frequently than other types of models when it comes to the raw food price index. This study also describes relevant data restrictions in wartime, and highlights avenues for improving the current suite of models for CPI forecasting.

Suggested Citation

  • Nadiia Shapovalenko, 2021. "A Suite of Models for CPI Forecasting," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 252, pages 4-36.
    • Handle: RePEc:ukb:journl:y:2021:i:252:p:4-36
    DOI: 10.26531/vnbu2021.252.01
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    File URL: https://journal.bank.gov.ua/en/article/2021/252/01
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    References listed on IDEAS

    as
    1. L. De Charsonville & F. Ferrière & C. Jardet, 2017. "MAPI: Model for Analysis and Projection of Inflation in France," Working papers 637, Banque de France.
    2. Luis J. Álvarez & Isabel Sánchez, 2017. "A suite of inflation forecasting models," Occasional Papers 1703, Banco de España.
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    Cited by:

    1. Dmytro Krukovets, 2024. "Exploring an LSTM-SARIMA routine for core inflation forecasting," Technology audit and production reserves, PC TECHNOLOGY CENTER, vol. 2(2(76)), pages 6-12, April.

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    More about this item

    Keywords

    short-term forecasting; forecast evaluation; CPI;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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