IDEAS home Printed from https://ideas.repec.org/a/agr/journl/vxxxiy2024i4(641)p109-118.html
   My bibliography  Save this article

Assessing the impact of energy and macroeconomic shocks on the Romanian economy: a Bayesian VAR approach

Author

Listed:
  • Alexandru George NEACȘU

    (Bucharest University of Economic Studies, Romania)

  • Andrei Costin NEACȘU

    (Bucharest University of Economic Studies, Romania)

  • Georgiana PLEȘA

    (Bucharest University of Economic Studies, Romania)

  • Georgian Dănuț MIHAI

    (Bucharest University of Economic Studies, Romania)

Abstract

This paper investigates the impact of both energy prices and macroeconomic variables on the Romanian economy using a Vector Autoregressive model (VAR), estimated using Bayesian inferences. Romania, a small emerging economy, has suffered considerable shocks in recent years, including the outbreak of the pandemic crisis, energy prices liberalization and the ongoing war in Ukraine. These shocks have increased the risk premium (through the exchange rate transmission channel), causing macroeconomic instability. The analysis investigates the transmission mechanism of both demand and supply shocks, with a focus on energy prices (oil and gas), inflation, economic growth, interest rate, real exchange rate and a sentiment indicator. Two separate models (including the oil price and, respectively, the gas price) show different dynamics in the responses of the Romanian macroeconomic variables.

Suggested Citation

  • Alexandru George NEACȘU & Andrei Costin NEACȘU & Georgiana PLEȘA & Georgian Dănuț MIHAI, 2024. "Assessing the impact of energy and macroeconomic shocks on the Romanian economy: a Bayesian VAR approach," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania / Editura Economica, vol. 0(4(641), W), pages 109-118, Winter.
  • Handle: RePEc:agr:journl:v:xxxi:y:2024:i:4(641):p:109-118
    as

    Download full text from publisher

    File URL: http://store.ectap.ro/articole/1789.pdf
    Download Restriction: no

    File URL: http://www.ectap.ro/articol.php?id=1789&rid=157
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    2. Inoue, Atsushi & Kilian, Lutz, 2013. "Inference on impulse response functions in structural VAR models," Journal of Econometrics, Elsevier, vol. 177(1), pages 1-13.
    3. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    4. Kim, Soyoung & Roubini, Nouriel, 2000. "Exchange rate anomalies in the industrial countries: A solution with a structural VAR approach," Journal of Monetary Economics, Elsevier, vol. 45(3), pages 561-586, June.
    5. Rachid Ouchchikh, 2017. "Monetary Policy Transmission Mechanism in a Small Open Economy under Fixed Exchange Rate: An SVAR Approach for Morocco," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 11(1), pages 42-51, December.
    6. Haroon Mumtaz & Paolo Surico, 2009. "The Transmission of International Shocks: A Factor-Augmented VAR Approach," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 41(s1), pages 71-100, February.
    7. Disyatat, Piti & Vongsinsirikul, Pinnarat, 2003. "Monetary policy and the transmission mechanism in Thailand," Journal of Asian Economics, Elsevier, vol. 14(3), pages 389-418, June.
    8. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    9. Korobilis, D & Pettenuzzo, D, 2016. "Adaptive Minnesota Prior for High-Dimensional Vector Autoregressions," Essex Finance Centre Working Papers 18626, University of Essex, Essex Business School.
    10. Caldara, Dario & Kamps, Christophe, 2008. "What are the effects of fiscal policy shocks? A VAR-based comparative analysis," Working Paper Series 877, European Central Bank.
    11. Chan, Joshua C.C., 2021. "Minnesota-type adaptive hierarchical priors for large Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1212-1226.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Martin, Gael M. & Frazier, David T. & Maneesoonthorn, Worapree & Loaiza-Maya, Rubén & Huber, Florian & Koop, Gary & Maheu, John & Nibbering, Didier & Panagiotelis, Anastasios, 2024. "Bayesian forecasting in economics and finance: A modern review," International Journal of Forecasting, Elsevier, vol. 40(2), pages 811-839.
    2. Gael M. Martin & David T. Frazier & Ruben Loaiza-Maya & Florian Huber & Gary Koop & John Maheu & Didier Nibbering & Anastasios Panagiotelis, 2023. "Bayesian Forecasting in the 21st Century: A Modern Review," Monash Econometrics and Business Statistics Working Papers 1/23, Monash University, Department of Econometrics and Business Statistics.
    3. Niko Hauzenberger & Florian Huber & Massimiliano Marcellino & Nico Petz, 2025. "Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 43(1), pages 27-43, January.
    4. Gary Koop & Dimitris Korobilis, 2019. "Forecasting with High‐Dimensional Panel VARs," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 81(5), pages 937-959, October.
    5. Miranda-Agrippino, Silvia & Ricco, Giovanni, 2018. "Bayesian Vector Autoregressions," The Warwick Economics Research Paper Series (TWERPS) 1159, University of Warwick, Department of Economics.
    6. Michael Pfarrhofer, 2024. "Forecasts with Bayesian vector autoregressions under real time conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(3), pages 771-801, April.
    7. Joshua C. C. Chan & Eric Eisenstat & Chenghan Hou & Gary Koop, 2020. "Composite likelihood methods for large Bayesian VARs with stochastic volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 35(6), pages 692-711, September.
    8. Koop, Gary & Korobilis, Dimitris & Pettenuzzo, Davide, 2019. "Bayesian compressed vector autoregressions," Journal of Econometrics, Elsevier, vol. 210(1), pages 135-154.
    9. Zeyyad Mandalinci & Haroon Mumtaz, 2019. "Global Economic Divergence and Portfolio Capital Flows to Emerging Markets," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 51(6), pages 1713-1730, September.
    10. Nima Nonejad, 2021. "An Overview Of Dynamic Model Averaging Techniques In Time‐Series Econometrics," Journal of Economic Surveys, Wiley Blackwell, vol. 35(2), pages 566-614, April.
    11. Fuentes-Albero, Cristina & Melosi, Leonardo, 2013. "Methods for computing marginal data densities from the Gibbs output," Journal of Econometrics, Elsevier, vol. 175(2), pages 132-141.
    12. Lian An & Xiaomei Ren & Huimin Li & Jing Xu, 2017. "Exchange Rate And Us Macroeconomy: Evidence From The Factor-Augmented Vector Autoregressive Model," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., vol. 62(02), pages 483-508, June.
    13. repec:hum:wpaper:sfb649dp2014-004 is not listed on IDEAS
    14. Lee, Seungyoon & Park, Jongwook, 2022. "Identifying monetary policy shocks using economic forecasts in Korea," Economic Modelling, Elsevier, vol. 111(C).
    15. Li, Huan & Ni, Jinlan & Xu, Yueli & Zhan, Minghua, 2021. "Monetary policy and its transmission channels: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    16. Vespignani, Joaquin L. & Ratti, Ronald A., 2016. "Not all international monetary shocks are alike for the Japanese economy," Economic Modelling, Elsevier, vol. 52(PB), pages 822-837.
    17. Zakaria Moussa, 2016. "How big is the comeback? Japanese exchange rate pass-through assessed by time-varying FAVAR," Post-Print hal-03714934, HAL.
    18. Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino & Michael Pfarrhofer, 2023. "Tail Forecasting With Multivariate Bayesian Additive Regression Trees," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(3), pages 979-1022, August.
    19. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    20. Lukas Berend & Jan Pruser, 2024. "The Transmission of Monetary Policy via Common Cycles in the Euro Area," Papers 2410.05741, arXiv.org, revised Nov 2024.
    21. Lutz Kilian, 2013. "Structural vector autoregressions," Chapters, in: Nigar Hashimzade & Michael A. Thornton (ed.), Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 22, pages 515-554, Edward Elgar Publishing.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:agr:journl:v:xxxi:y:2024:i:4(641):p:109-118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mircea Dinu (email available below). General contact details of provider: https://edirc.repec.org/data/agerrea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.