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Analysts' Inflation Expectations vs Univariate Models of Inflation Forecasting in the Russian Economy

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  • Yury Perevyshin

    (RANEPA)

Abstract

This paper analyses the accuracy of analysts' inflation expectations from the consensus forecast of the Centre of Development Institute of the Higher School of Economics, which is used as a direct forecast of inflation. The consensus forecast is inferior in accuracy to univariate econometric forecasting models on horizons of six to eight quarters and is no more accurate than the model forecasts on shorter horizons. The medium-term expectations of professional forecasters for the Russian economy have been anchored to the Bank of Russia's target of 4% since 2017. The use of analysts' inflation expectations does not lead to a significant improvement in the accuracy of the inflation forecast in the Phillips curve framework in the Russian economy over the past five years. Iterative forecasting of inflation via the Phillips curve turns out to be more accurate than direct forecasts, first-order vector autoregression model, or random walk model.

Suggested Citation

  • Yury Perevyshin, 2024. "Analysts' Inflation Expectations vs Univariate Models of Inflation Forecasting in the Russian Economy," Russian Journal of Money and Finance, Bank of Russia, vol. 83(2), pages 54-76, June.
    • Handle: RePEc:bkr:journl:v:83:y:2024:i:2:p:54-76
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    References listed on IDEAS

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    1. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    2. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    3. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    4. Bharat Trehan, 2015. "Survey Measures of Expected Inflation and the Inflation Process," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(1), pages 207-222, February.
    5. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
    6. Baumann, Ursel & Darracq Pariès, Matthieu & Westermann, Thomas & Riggi, Marianna & Bobeica, Elena & Meyler, Aidan & Böninghausen, Benjamin & Fritzer, Friedrich & Trezzi, Riccardo & Jonckheere, Jana & , 2021. "Inflation expectations and their role in Eurosystem forecasting," Occasional Paper Series 264, European Central Bank.
    7. Hubrich, Kirstin, 2005. "Forecasting euro area inflation: Does aggregating forecasts by HICP component improve forecast accuracy?," International Journal of Forecasting, Elsevier, vol. 21(1), pages 119-136.
    8. Michael D. Bauer & Erin McCarthy, 2015. "Can we rely on market-based inflation forecasts?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    9. Konstantin Styrin, 2019. "Forecasting Inflation in Russia Using Dynamic Model Averaging," Russian Journal of Money and Finance, Bank of Russia, vol. 78(1), pages 3-18, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    inflation expectations; inflation; monetary policy; Phillips curve; inflation forecasting; univariate time series models; forecast accuracy;
    All these keywords.

    JEL classification:

    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations

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