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Stylized facts of business cycles in a transition economy in time and frequency

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  • Caraiani, Petre

Abstract

We discuss the properties of business cycles in Romania between 1991 and 2011 using a wavelets based method. We compare it with the standard approaches in literature. We analyze the relationship between output and key macroeconomic variables in time and frequency. While in terms of comovement the results are mostly in line with previous findings, there is a better picture of the time varying features as well as the influence of recessions on the nature of the relationships. The approach applied here can also isolate the influence of specific events, like the accession to the European Union.

Suggested Citation

  • Caraiani, Petre, 2012. "Stylized facts of business cycles in a transition economy in time and frequency," Economic Modelling, Elsevier, vol. 29(6), pages 2163-2173.
  • Handle: RePEc:eee:ecmode:v:29:y:2012:i:6:p:2163-2173
    DOI: 10.1016/j.econmod.2012.06.014
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    References listed on IDEAS

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    Cited by:

    1. Verona, Fabio, 2016. "Time–frequency characterization of the U.S. financial cycle," Economics Letters, Elsevier, vol. 144(C), pages 75-79.
    2. Özmen, M. Utku & Yılmaz, Erdal, 2017. "Co-movement of exchange rates with interest rate differential, risk premium and FED policy in “fragile economies”," Emerging Markets Review, Elsevier, vol. 33(C), pages 173-188.
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    4. repec:eee:ecmode:v:65:y:2017:i:c:p:51-66 is not listed on IDEAS
    5. Aviral Tiwari & Niyati Bhanja & Arif Dar & Faridul Islam, 2015. "Time–frequency relationship between share prices and exchange rates in India: Evidence from continuous wavelets," Empirical Economics, Springer, vol. 48(2), pages 699-714, March.
    6. Funashima, Yoshito, 2016. "Governmentally amplified output volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 462(C), pages 469-478.
    7. Funashima, Yoshito, 2017. "Time-varying leads and lags across frequencies using a continuous wavelet transform approach," Economic Modelling, Elsevier, vol. 60(C), pages 24-28.
    8. repec:eee:ecmode:v:67:y:2017:i:c:p:261-274 is not listed on IDEAS
    9. Bilgili, Faik, 2015. "Business cycle co-movements between renewables consumption and industrial production: A continuous wavelet coherence approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 325-332.
    10. Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu, 2016. "Oil price and exchange rate in India: Fresh evidence from continuous wavelet approach and asymmetric, multi-horizon Granger-causality tests," Applied Energy, Elsevier, vol. 179(C), pages 272-283.
    11. Fousekis, Panos & Grigoriadis, Vasilis, 2016. "Spatial price dependence by time scale: Empirical evidence from the international butter markets," Economic Modelling, Elsevier, vol. 54(C), pages 195-204.
    12. repec:eee:reveco:v:49:y:2017:i:c:p:484-498 is not listed on IDEAS
    13. repec:eee:finlet:v:24:y:2018:i:c:p:19-24 is not listed on IDEAS
    14. Rita Sousa & Luís Aguiar-Conraria & Maria Joana Soares, 2014. "Carbon Financial Markets: a time-frequency analysis of CO2 price drivers," NIPE Working Papers 03/2014, NIPE - Universidade do Minho.
    15. Luís Aguiar-Conraria & Maria Joana Soares, 2014. "The Continuous Wavelet Transform: Moving Beyond Uni- And Bivariate Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 28(2), pages 344-375, April.

    More about this item

    Keywords

    Business cycles; Filters; Wavelets;

    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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