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Greek GDP Forecasting Using Bayesian Multivariate Models

Author

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  • Zacharias Bragoudakis
  • Ioannis Krompas

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-VAR). The BVAR is found to have statistically significant forecasting gains against the benchmark and the TVP-VAR. Furthermore, the BVAR requires only minimal modifications to account for the effect of pandemic observations on its coefficients, only for longer-term forecasts.

Suggested Citation

  • Zacharias Bragoudakis & Ioannis Krompas, 2025. "Greek GDP Forecasting Using Bayesian Multivariate Models," Bulletin of Applied Economics, Risk Market Journals, vol. 12(2), pages 63-76.
    • Handle: RePEc:rmk:rmkbae:v:12:y:2025:i:2:p:63-76
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    Keywords

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    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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