Greek GDP forecasting using Bayesian multivariate models

Zacharias Bragoudakis () and Ioannis Krompas
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Ioannis Krompas: NBG Economic Research

No 321, Working Papers from Bank of Greece

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.

Keywords: Bayesian VARs; Forecasting; GDP; TVP-VAR; VECM (search for similar items in EconPapers)
JEL-codes: C11 C51 C52 C53 (search for similar items in EconPapers)
Pages: 21
Date: 2023-06
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