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Bidirectional linkage between inflation and inflation uncertainty – the case of Eastern European countries

Author

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  • Dejan Živkov

    (Novi Sad Business School, University of Novi Sad, Novi Sad, Serbia)

  • Jovan Njegic

    (Novi Sad Business School, University of Novi Sad, Novi Sad, Serbia)

  • Marko Pecanac

    (Cabinet of Serbian Prime Minister, Serbia)

Abstract

This paper explores bidirectional linkage between inflation and its uncertainty by observing monthly data of 11 Eastern European countries. The methodological approach comprises two steps. First, inflation uncertainty series have been created by choosing an optimal Generalized Autoregressive Conditional Heteroskedasticity- (GARCH) type model. Subsequently, inflation and inflation uncertainty have been observed together by two models examining whether Friedman's and Cukierman–Meltzer's hypotheses hold for selected Eastern Europe Countries (EEC). Due to the heterogeneous behaviour of some series of inflation and inflation uncertainty, the unconditional quantile regression estimation technique has been applied because of its robustness to the particular non-normal characteristics and outliers’ presence in the empirical data. According to the findings, both Friedman's and Cukierman–Meltzer's hypotheses have been confirmed primarily for the largest EEC with flexible exchange rate. In contrast, these theories are refuted in smaller, open economies with firm exchange rate regime.

Suggested Citation

  • Dejan Živkov & Jovan Njegic & Marko Pecanac, 2014. "Bidirectional linkage between inflation and inflation uncertainty – the case of Eastern European countries," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 14(1-2), pages 124-139, December.
  • Handle: RePEc:bic:journl:v:14:y:2014:i:1-2:p:124-139
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    References listed on IDEAS

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    2. Claudiu T. Albulescu & Aviral Kumar Twari & Stephen M. Miller & Rangan Gupta, 2015. "Time-Frequency Relationship between Inflation and Inflation Uncertainty for the U.S.: Evidence from Historical Data," Working Papers 201591, University of Pretoria, Department of Economics.
    3. Baharumshah, Ahmad Zubaidi & Slesman, Ly & Wohar, Mark E., 2016. "Inflation, inflation uncertainty, and economic growth in emerging and developing countries: Panel data evidence," Economic Systems, Elsevier, vol. 40(4), pages 638-657.
    4. Haryo Kuncoro, 2024. "Inflation and Its Uncertainty: Evidence from Indonesia and the Philippines," Global Journal of Emerging Market Economies, Emerging Markets Forum, vol. 16(2), pages 231-247, May.
    5. Jasmina Ðuraškovic & Slavica Manic & Dejan Živkov, 2019. "Multiscale Volatility Transmission and Portfolio Construction Between the Baltic Stock Markets," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 69(2), pages 211-235, April.
    6. Tariq A.H. Al-Zuhd & Mohammad H. Saleh, 2017. "Inflation and Inflation Uncertainty Nexus in Kuwait: A GARCH Modeling Approach," International Journal of Economics and Financial Issues, Econjournals, vol. 7(5), pages 198-203.
    7. Claudiu Tiberiu Albulescu & Aviral Kumar Tiwari & Stephen M. Miller & Rangan Gupta, 2019. "Time–frequency relationship between US inflation and inflation uncertainty: evidence from historical data," Scottish Journal of Political Economy, Scottish Economic Society, vol. 66(5), pages 673-702, November.
    8. Jiranyakul, Komain, 2020. "The Linkages between Inflation and Inflation Uncertainty in Selected Asian Economies: Evidence from Quantile Regression," MPRA Paper 99868, University Library of Munich, Germany.
    9. Dejan Živkov & Boris Kuzman & Jonel Subić, 2020. "What Bayesian quantiles can tell about volatility transmission between the major agricultural futures?," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 66(5), pages 215-225.
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    More about this item

    Keywords

    inflation; inflation uncertainty; GARCH; quantile regression; Eastern European countries;
    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
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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