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The Bulgarian Economy – Trends and Signals in the Context of Global Challenges

Author

Listed:
  • Yanko Hristozov

    (University of National and World Economy, Sofia, Bulgaria)

  • Daniel Kasabov

    (Department of Economics, Sofia University, Bulgaria)

  • Diyana Miteva

    (University of National and World Economy, Sofia, Bulgaria)

Abstract

This article examines the key macroeconomic trends and early warning signals for the Bulgarian economy in 2023– 2024, in the context of an unstable external environment. The study aims to identify the main drivers of growth, assess inflationary dynamics, and evaluate the applicability of an early warning indicator. The analysis is quantitative and empirical, combining descriptive and comparative examination of official data with econometric modeling using a Markov-switching dynamic factor model. The findings show that GDP growth slowed from 4.0% in 2022 to 1.9% in 2023, before rebounding to 2.8% in 2024, driven primarily by domestic demand, while external demand weakened. Inflation declined from 13.0% in 2022 to 3.1% by the end of 2024; however, price pressures in the services sector remained elevated due to rising wages and labor costs. The proposed early warning indicator, constructed from five high-frequency variables, successfully signaled a 70% probability of low growth in mid-2023, which decreased to 55% by the end of 2024. These results highlight the value of high-frequency data and advanced modeling techniques for timely forecasting of cyclical fluctuations.

Suggested Citation

  • Yanko Hristozov & Daniel Kasabov & Diyana Miteva, 2025. "The Bulgarian Economy – Trends and Signals in the Context of Global Challenges," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 4, pages 1026-1059, Desember.
    • Handle: RePEc:nwe:eajour:y:2025:i:4:p:1026-1059
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    References listed on IDEAS

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    1. Roberto S. Mariano & Yasutomo Murasawa, 2003. "A new coincident index of business cycles based on monthly and quarterly series," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(4), pages 427-443.
    2. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    3. Camacho, Maximo & Perez-Quiros, Gabriel & Poncela, Pilar, 2018. "Markov-switching dynamic factor models in real time," International Journal of Forecasting, Elsevier, vol. 34(4), pages 598-611.
    4. James H. Stock & Mark W. Watson, 1989. "New Indexes of Coincident and Leading Economic Indicators," NBER Chapters, in: NBER Macroeconomics Annual 1989, Volume 4, pages 351-409, National Bureau of Economic Research, Inc.
    5. Chauvet, Marcelle & Piger, Jeremy, 2008. "A Comparison of the Real-Time Performance of Business Cycle Dating Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 42-49, January.
    6. Hamilton, James D., 2011. "Calling recessions in real time," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1006-1026, October.
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    More about this item

    Keywords

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    JEL classification:

    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis

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