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Modeling Business Cycles In The Romanian Economy Using The Markov Switching Approach

Author

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  • Caraiani, Petre

    (Institute for Economic Forecasting, Romanian Academy.)

Abstract

I use the Markov Switching AR approach to model the business cycles in Romanian economy for the 1991-2008 period using monthly data on industrial production. The time series used allows for a comparison with previous dating of Romanian business cycles. Generally, the MS-AR performs well, confirming the previous finding about turning points in business cycles during the transition period. At the same time, it suggests that the ongoing recession started earlier than conventionally thought and that it may last more than a year and a half.

Suggested Citation

  • Caraiani, Petre, 2010. "Modeling Business Cycles In The Romanian Economy Using The Markov Switching Approach," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 130-136, March.
    • Handle: RePEc:rjr:romjef:v::y:2010:i:1:p:130-136
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    References listed on IDEAS

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    2. Purica, Ionut & Caraiani, Petre, 2009. "Second Order Dynamics Of Economic Cycles," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 6(1), pages 36-47, March.
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    5. Caraiani, Petre, 2004. "Nominal And Real Stylized Facts Of The Business Cycles In Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 1(4), pages 121-132, December.
    6. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    7. Albu, Lucian Liviu, 2001. "Evolution Of Inflation-Unemployment Relationship In The Perspective Of Romania’S Accession To Eu," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 5-23, December.
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    Cited by:

    1. Kamel Helali, 2022. "Markov Switching-Vector AutoRegression Model Analysis of the Economic and Growth Cycles in Tunisia and Its Main European Partners," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 13(1), pages 656-686, March.
    2. Shirly Siew-Ling WONG & Chin-Hong PUAH & Shazali ABU MANSOR & Venus Khim-Sen LIEW, 2016. "Measuring Business Cycle Fluctuations: An Alternative Precursor To Economic Crises," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 235-248.
    3. Grecu Robert-Adrian, 2022. "Synchronization of Business Cycles in European Union Countries," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 16(1), pages 217-228, August.
    4. Kutu Adebayo Augustine & Ngalawa Harold, 2017. "Monetary Policy and Industrial Output in the BRICS Countries: A Markov-Switching Model," Folia Oeconomica Stetinensia, Sciendo, vol. 17(2), pages 35-55, December.
    5. Cristi SPULBAR & Mihai NITOI & Cristian STANCIU, 2012. "Identifying The Industry Business Cycle Using The Markov Switching Approach In Central And Eastern Europe," Management and Marketing Journal, University of Craiova, Faculty of Economics and Business Administration, vol. 0(2), pages 293-300, November.

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    More about this item

    Keywords

    business cycles; Markov switching; nonlinear methods; transition economies; mathematical methods;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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