Greek GDP Forecasting Using Bayesian Multivariate Models

Zacharias Bragoudakis and Ioannis Krompas

Bulletin of Applied Economics, 2025, vol. 12, issue 2, 63-76

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.

Keywords: Bayesian VARs; Forecasting; GDP; VECM. (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 C53 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:rmk:rmkbae:v:12:y:2025:i:2:p:63-76

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