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A Bayesian VAR benchmark for COMPASS

Author

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  • Domit, Sílvia

    (Bank of England)

  • Monti, Francesca

    (Bank of England)

  • Sokol, Andrej

    (Bank of England)

Abstract

We estimate a Bayesian VAR analogue to the Bank of England’s DSGE model (COMPASS) and assess their relative performance in forecasting GDP growth and CPI inflation in real time between 2000 and 2012. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, the performance of these models was similar when forecasting CPI. We also find that, despite underpredicting inflation at most forecast horizons, the BVAR density forecasts outperformed those of COMPASS. Both models overpredicted GDP growth at all forecast horizons, but the BVAR outperformed COMPASS at forecast horizons up to one year ahead. The BVAR’s point and density forecast performance is also comparable to that of a Bank of England in-house statistical suite for both GDP and CPI inflation and to the Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.

Suggested Citation

  • Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2016. "A Bayesian VAR benchmark for COMPASS," Bank of England working papers 583, Bank of England.
  • Handle: RePEc:boe:boeewp:0583
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    References listed on IDEAS

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    Cited by:

    1. Olga Korotkikh, 2020. "A Multi-Country BVAR Model for the External Sector," Russian Journal of Money and Finance, Bank of Russia, vol. 79(4), pages 98-112, December.
    2. Ashwin Madhou & Tayushma Sewak & Imad Moosa & Vikash Ramiah, 2017. "GDP nowcasting: application and constraints in a small open developing economy," Applied Economics, Taylor & Francis Journals, vol. 49(38), pages 3880-3890, August.
    3. Dmitry Kreptsev & Sergei Seleznev, 2017. "DSGE Model of the Russian Economy with the Banking Sector," Bank of Russia Working Paper Series wps27, Bank of Russia.
    4. Dmitry Kreptsev & Sergei Seleznev, 2018. "Forecasting for the Russian Economy Using Small-Scale DSGE Models," Russian Journal of Money and Finance, Bank of Russia, vol. 77(2), pages 51-67, June.
    5. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    6. Nasir, Muhammad Ali, 2020. "Forecasting inflation under uncertainty: The forgotten dog and the frisbee," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Ian Borg & Germano Ruisi, 2018. "Forecasting using Bayesian VARs: A Benchmark for STREAM," CBM Working Papers WP/04/2018, Central Bank of Malta.
    8. Angelini, Elena & Lalik, Magdalena & Lenza, Michele & Paredes, Joan, 2019. "Mind the gap: A multi-country BVAR benchmark for the Eurosystem projections," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1658-1668.

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    More about this item

    Keywords

    Forecasting; Bayesian VARs; macro-modelling;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E12 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Keynes; Keynesian; Post-Keynesian; Modern Monetary Theory
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications

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