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Greek GDP forecasting using Bayesian multivariate models

Author

Listed:
  • Zacharias Bragoudakis

    (Bank of Greece and National and Kapodistrian University of Athens)

  • Ioannis Krompas

    (NBG Economic Research)

Abstract

Building on a proper selection of macroeconomic variables for constructing a Gross Domestic Product (GDP) forecasting multivariate model (Kazanas, 2017), this paper evaluates whether alternative Bayesian model specifications can provide greater forecasting accuracy compared to a standard Vector Error Correction model (VECM). To that end, two Bayesian Vector Autoregression models (BVARs) are estimated, a BVAR using Litterman’s prior (1979) and a BVAR with time-varying parameters (TVP-BVAR). Two forecasting evaluation exercises are then carried out, a 28-quarters ahead forecast and a recursive 4-quarters ahead forecast. The BVAR outperformed the other models in the first, whereas the TVP-VAR was the best-performing model in the second, highlighting the importance of having adjusting mechanisms, such as time-varying coefficients in a model.

Suggested Citation

  • Zacharias Bragoudakis & Ioannis Krompas, 2023. "Greek GDP forecasting using Bayesian multivariate models," Working Papers 321, Bank of Greece.
    • Handle: RePEc:bog:wpaper:321
    DOI: 10.52903/wp2023321
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    More about this item

    Keywords

    Bayesian VARs; Forecasting; GDP; TVP-VAR; VECM;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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

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