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Forecasting using Bayesian VARs: A Benchmark for STREAM

Ian Borg () and Germano Ruisi
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Ian Borg: Central Bank of Malta

No WP/04/2018, CBM Working Papers from Central Bank of Malta

Abstract: This study develops a suite of Bayesian Vector Autoregression (BVAR) models for the Maltese economy to benchmark the forecasting performance of STREAM, the traditional macro-econometric model used by the Central Bank of Malta for its regular forecasting exercises. Three different BVARs are proposed, containing an endogenous and exogenous block, and differ only in terms of the crosssectional size of the former. The small BVAR contains only three endogenous variables, the medium BVAR includes 17 variables, while the large BVAR includes 32 endogenous variables. The exogenous block remains consistent across the three models. By using a similar information set, the Bayesian VARs developed in this study are utilised to benchmark the forecast performance of STREAM. In general, for real GDP, the GDP deflator, and the unemployment rate, BVAR median projections for the period 2014-2016 improve the forecast performance at the one, two, and four-step ahead horizons when compared to STREAM. However, the latter does rather well at annual projections, but it is broadly outperformed by the medium and large BVARs.

JEL-codes: C11 C52 C53 C55 E17 (search for similar items in EconPapers)
Pages: 27 pgs
Date: 2018
New Economics Papers: this item is included in nep-for, nep-mac and nep-ore
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