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Determinants of Low Inflation in an Emerging, Small Open Economy through the Lens of Aggregated and Disaggregated Approach

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  • Karol Szafranek
  • Aleksandra Hałka

Abstract

We analyze the sources of the protracted period of exceptionally low inflation in the emerging, small open economy of Poland using a structural Bayesian Vector Autoregression (BVAR) identified with a mixture of zero and sign restrictions. We find that excessive disinflation has been caused by deteriorating domestic conditions whilst deflation has resulted from the convolution of waning global demand and plummeting oil prices. Disaggregated analysis corroborates the conclusion from the aggregated approach but reveals considerable heterogeneities in the sensitivity of inflation components to the identified shocks. We conclude that the structural analysis on the disaggregated price indices unveils additional information for the monetary policy conduct.

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  • Karol Szafranek & Aleksandra Hałka, 2019. "Determinants of Low Inflation in an Emerging, Small Open Economy through the Lens of Aggregated and Disaggregated Approach," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(13), pages 3094-3111, October.
    • Handle: RePEc:mes:emfitr:v:55:y:2019:i:13:p:3094-3111
    DOI: 10.1080/1540496X.2018.1541793
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    Cited by:

    1. Jakšić Saša, 2022. "Modelling Determinants of Inflation in CESEE Countries: Global Vector Autoregressive Approach," Review of Economic Perspectives, Sciendo, vol. 22(1), pages 137-169, June.
    2. Rybinski, Krzysztof, 2021. "Ranking professional forecasters by the predictive power of their narratives," International Journal of Forecasting, Elsevier, vol. 37(1), pages 186-204.
    3. Ahmad Zubaidi Baharumshah & Siew-Voon Soon & Mark E. Wohar, 2021. "Phillips Curve for the Asian Economies: A Nonlinear Perspective," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 57(12), pages 3508-3537, September.
    4. Julius Stakenas, 2018. "Slicing up inflation: analysis and forecasting of Lithuanian inflation components," Bank of Lithuania Working Paper Series 56, Bank of Lithuania.
    5. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    6. Paweł Macias & Damian Stelmasiak, 2019. "Food inflation nowcasting with web scraped data," NBP Working Papers 302, Narodowy Bank Polski.
    7. Conti, Antonio M., 2021. "Resurrecting the Phillips Curve in Low-Inflation Times," Economic Modelling, Elsevier, vol. 96(C), pages 172-195.
    8. Mariusz Kapuściński, 2018. "How far does monetary policy reach? Evidence from factor-augmented vector autoregressions for Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 49(3), pages 191-216.
    9. Szafranek, Karol, 2021. "Disentangling the sources of inflation synchronization. Evidence from a large panel dataset," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 229-245.
    10. Szafranek, Karol, 2021. "Evidence on time-varying inflation synchronization," Economic Modelling, Elsevier, vol. 94(C), pages 1-13.

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    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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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