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Inflation Forecasting in Turbulent Times

Author

Listed:
  • Martin, Ertl

    (Institute for Advanced Studies Vienna, Austria)

  • Fortin, Ines

    (Institute for Advanced Studies Vienna, Austria)

  • Hlouskova, Jaroslava

    (Institute for Advanced Studies Vienna, Austria)

  • Koch, Sebastian P.

    (Institute for Advanced Studies Vienna, Austria)

  • Kunst, Robert M.

    (Institute for Advanced Studies Vienna, Austria)

  • Soegner, Leopold

    (Institute for Advanced Studies Vienna, Austria and Vienna Graduate School of Finance (VGSF))

Abstract

Recently, many countries were hit by a series of macroeconomic shocks, most notably as a consequence of the COVID-19 pandemic and Russia’s invasion in Ukraine, raising inflation rates to multi-decade highs and suspending well-documented macroeconomic relationships. To capture these tail events, we propose a mixed-frequency Bayesian vector autoregressive (BVAR) model with t-distributed innovations or with stochastic volatility. While inflation, industrial production, oil and gas prices are available at monthly frequencies, real gross domestic product (GDP) is observed at a quarterly frequency. Thus, we apply a mixed-frequency framework using the forward-filtering-backward-sampling algorithm to generate monthly real GDP growth rates. We forecast inflation in those euro area countries which extensively import energy from Russia and therefore have been heavily exposed to the recent oil and gas price shocks. To measure the forecast performance of our mixed-frequency BVAR model, we compare these inflation forecasts with those generated by a battery of competing inflation forecasting models. The proposed BVAR models dominate the competition for all countries in terms of the log predictive density score.

Suggested Citation

  • Martin, Ertl & Fortin, Ines & Hlouskova, Jaroslava & Koch, Sebastian P. & Kunst, Robert M. & Soegner, Leopold, 2024. "Inflation Forecasting in Turbulent Times," IHS Working Paper Series 56, Institute for Advanced Studies.
  • Handle: RePEc:ihs:ihswps:number56
    as

    Download full text from publisher

    File URL: https://irihs.ihs.ac.at/id/eprint/7048
    File Function: First version, 2024
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    Other versions of this item:

    • Martin Ertl & Ines Fortin & Jaroslava Hlouskova & Sebastian P. Koch & Robert M. Kunst & Leopold Sögner, 2025. "Inflation forecasting in turbulent times," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 52(1), pages 5-37, February.

    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Bayesian VAR; mixed-frequency; forward-filtering-backward-sampling; inflation forecasting;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles

    NEP fields

    This paper has been announced in the following NEP Reports:

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