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Forecasting the UK economy with a medium-scale Bayesian VAR

Sílvia Domit, Francesca Monti () and Andrej Sokol ()

International Journal of Forecasting, 2019, vol. 35, issue 4, 1669-1678

Abstract: We estimate a Bayesian VAR (BVAR) for the UK economy and assess its performance in forecasting GDP growth and CPI inflation in real time relative to forecasts from COMPASS, the Bank of England’s DSGE model, and other benchmarks. We find that the BVAR outperformed COMPASS when forecasting both GDP and its expenditure components. In contrast, their performances when forecasting CPI were similar. We also find that the BVAR density forecasts outperformed those of COMPASS, despite under-predicting inflation at most forecast horizons. Both models over-predicted GDP growth at all forecast horizons, but the issue was less pronounced in the BVAR. The BVAR’s point and density forecast performances are also comparable to those of a Bank of England in-house statistical suite for both GDP and CPI inflation, as well as to the official Inflation Report projections. Our results are broadly consistent with the findings of similar studies for other advanced economies.

Keywords: Macroeconomic forecasting; Bayesian methods; Vector autoregression models; Econometric models; Inflation forecasting (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1669-1678

DOI: 10.1016/j.ijforecast.2018.11.004

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