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Forecasting Euro Area Aggregates with Bayesian VAR and VECM Models

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Abstract

This paper focuses on Bayesian Vector Auto-Regressive (BVAR) models for the euro area. A modified hyperparameterization scheme based on the Minnesota prior that takes into account the economic nature of the variables in the model is used. The merits of incorporating long-run relationships are also discussed. Alternative methods to estimate eventual cointegrating relations in the variables are considered, and the problem of choice of appropriate prior distributions for BVAR with Error Correction Mechanism (BECM) models is addressed. Results show that using a flat prior on factor loadings can seriously endanger the forecasting performance of BECM models. Overall, the BVAR model in levels outperforms all other models across variables and forecasting horizons. This is in contrast with other empirical studies where some gains could be obtained when incorporating long-run relationships in the model.

Suggested Citation

  • Luís Catela Nunes, 2003. "Forecasting Euro Area Aggregates with Bayesian VAR and VECM Models," Working Papers w200304, Banco de Portugal, Economics and Research Department.
    • Handle: RePEc:ptu:wpaper:w200304
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    Cited by:

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    2. Saker Sabkha & Christian de Peretti & Dorra Mezzez Hmaied, 2019. "International risk spillover in the sovereign credit markets: An empirical analysis," Post-Print hal-01652526, HAL.
    3. Ahmed, Abdullahi D. & Huo, Rui, 2019. "Impacts of China's crash on Asia-Pacific financial integration: Volatility interdependence, information transmission and market co-movement," Economic Modelling, Elsevier, vol. 79(C), pages 28-46.
    4. Saker Sabkha & Christian de Peretti & Dorra Hmaied, 2017. "International risk spillover in the sovereign credit markets: An empirical analysis," Working Papers hal-01652526, HAL.
    5. Branimir Jovanovic & Magdalena Petrovska, 2010. "Forecasting Macedonian GDP: Evaluation of different models for short-term forecasting," Working Papers 2010-02, National Bank of the Republic of North Macedonia, revised Aug 2010.
    6. 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.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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