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Forecasting using Bayesian VARs: A Benchmark for STREAM

Author

Listed:
  • Ian Borg

    (Central Bank of Malta)

  • Germano Ruisi

Abstract

This study develops a suite of Bayesian Vector Autoregression (BVAR) models for the Maltese economy to benchmark the forecasting performance of STREAM, the traditional macro-econometric model used by the Central Bank of Malta for its regular forecasting exercises. Three different BVARs are proposed, containing an endogenous and exogenous block, and differ only in terms of the crosssectional size of the former. The small BVAR contains only three endogenous variables, the medium BVAR includes 17 variables, while the large BVAR includes 32 endogenous variables. The exogenous block remains consistent across the three models. By using a similar information set, the Bayesian VARs developed in this study are utilised to benchmark the forecast performance of STREAM. In general, for real GDP, the GDP deflator, and the unemployment rate, BVAR median projections for the period 2014-2016 improve the forecast performance at the one, two, and four-step ahead horizons when compared to STREAM. However, the latter does rather well at annual projections, but it is broadly outperformed by the medium and large BVARs.

Suggested Citation

  • Ian Borg & Germano Ruisi, 2018. "Forecasting using Bayesian VARs: A Benchmark for STREAM," CBM Working Papers WP/04/2018, Central Bank of Malta.
    • Handle: RePEc:mlt:wpaper:0418
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    File URL: https://www.centralbankmalta.org/file.aspx?f=71871
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    References listed on IDEAS

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    1. Marta Bańbura, 2008. "Large Bayesian VARs," 2008 Meeting Papers 334, Society for Economic Dynamics.
    2. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
    3. Hélène Rey, 2016. "International Channels of Transmission of Monetary Policy and the Mundellian Trilemma," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 64(1), pages 6-35, May.
    4. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    5. Bloor, Chris & Matheson, Troy, 2011. "Real-time conditional forecasts with Bayesian VARs: An application to New Zealand," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 26-42, January.
    6. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    7. Rossi, Barbara & Gürkaynak, Refet & Kısacıkoğlu, Burçin, 2013. "Do DSGE Models Forecast More Accurately Out-of-Sample than VAR Models?," CEPR Discussion Papers 9576, C.E.P.R. Discussion Papers.
    8. 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.
    9. Hubrich, Kirstin & Karlsson, Tohmas, 2010. "Trade consistency in the context of the Eurosystem projection exercises – an overview," Occasional Paper Series 108, European Central Bank.
    10. Danilo Leiva-Leon, 2017. "Monitoring the Spanish Economy through the Lenses of Structural Bayesian VARs," Occasional Papers 1706, Banco de España.
    11. Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2016. "A Bayesian VAR benchmark for COMPASS," Bank of England working papers 583, Bank of England.
    12. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    13. Piergiorgio Alessandri & Haroon Mumtaz, 2017. "Financial conditions and density forecasts for US output and inflation," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 24, pages 66-78, March.
    14. repec:pra:mprapa:46125 is not listed on IDEAS
    15. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, vol. 84(Q1), pages 4-18.
    16. Hubrich, Kirstin & Karlsson, Tohmas, 2010. "Trade consistency in the context of the Eurosystem projection exercises - an overview," Occasional Paper Series 108, European Central Bank.
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    Cited by:

    1. Rueben Ellul & Germano Ruisi, 2022. "Nowcasting the Maltese economy with a dynamic factor model," CBM Working Papers WP/02/2022, Central Bank of Malta.

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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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