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Forecasting Romanian GDP Using a BVAR Model

Author

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  • Caraiani, Petre

    (Institute for Economic Forecasting, Romanian Academy)

Abstract

In this study I use the Bayesian VAR framework to forecast the dynamics of output for the Romanian economy. I estimate several versions of Bayesian VARs and compare them in terms of forecasting statistics with two standard models, the OLS and the unrestricted VAR, as well as with a naïve forecast. The findings confirm that the BVAR approach outperforms the standard models. The best BVAR model is used for forecasting quarterly GDP in the short run. The results show that the recovery will be slow and that the output gap will continue to be negative for a few quarters even after the economy starts to grow.

Suggested Citation

  • Caraiani, Petre, 2010. "Forecasting Romanian GDP Using a BVAR Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 76-87, December.
    • Handle: RePEc:rjr:romjef:v::y:2010:i:4:p:76-87
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    References listed on IDEAS

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    Cited by:

    1. Spulbăr Cristi & Niţoi Mihai, 2013. "Monetary Policy Transmission Mechanism in Romania Over the Period 2001 to 2012: A Bvar Analysis," Scientific Annals of Economics and Business, Sciendo, vol. 60(2), pages 1-12, December.
    2. Dedu, Vasile & Stoica, Tiberiu, 2014. "The Impact of Monetaru Policy on the Romanian Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 71-86, June.
    3. Margarit Monica-Ionelia, 2022. "Using The Bayesian Var In Monetary Policy Analysis: A Literature Review," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 212-216, February.
    4. Ngomba Bodi, Francis Ghislain & Bikai, Landry, 2017. "Prévisions de l’inflation et de la croissance en zone CEMAC [Inflation and real growth forecasts in CEMAC zone]," MPRA Paper 116433, University Library of Munich, Germany.
    5. Abuselidze, George & Mamaladze, Lela, 2019. "U.S-Turkey Crisis and Its Impact on the Economy of the Black Sea Region," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 135.
    6. Andrei, Dalina Maria, 2012. "Foreign Direct Investments in Romania. A Structural and Dynamic View," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 129-146, December.
    7. Anastasiia Pankratova, 2024. "Forecasting Key Macroeconomic Indicators Using DMA and DMS Methods," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 32-52, March.
    8. Mihaela SIMIONESCU, 2015. "Is Africa’s current growth reducing inequality? Evidence from some selected african countries," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 3(1), pages 68-74, June.
    9. Usman Shakoor & Mudassar Rashid & Ashfaque Ali Baloch & Muhammad Iftikhar ul Husnain & Abdul Saboor, 2021. "How Aging Population Affects Health Care Expenditures in Pakistan? A Bayesian VAR Analysis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 153(2), pages 585-607, January.

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    More about this item

    Keywords

    forecasting methods; VAR models; Bayesian methods; simulation methods;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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

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