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Do we need a global VAR model to forecast inflation and output in South Africa?

Annari De Waal, Renee van Eyden () and Rangan Gupta ()

No 201346, Working Papers from University of Pretoria, Department of Economics

Abstract: This study determines whether the global vector autoregressive (GVAR) approach provides better forecasts of key South African variables than a vector error correction model (VECM) and a Bayesian vector autoregressive (BVAR) model augmented with foreign variables. The paper considers both a small GVAR model and a large GVAR model in determining the most appropriate model for forecasting South African variables. We compare the recursive out-of-sample forecasts for South African GDP and inflation from six types of models: a general 33-country (large) GVAR, a customised small GVAR for South Africa, a VECM for South Africa with weakly exogenous foreign variables, a BVAR model, autoregressive (AR) models and random walk models. The results show that the forecast performance of the large GVAR is generally superior to the performance of the customised small GVAR for South Africa. The forecasts of both the GVAR models tend to be better than the forecasts of the augmented VECM, especially at longer forecast horizons. Importantly however, on average, the BVAR model performs the best when it comes to forecasting output, while the AR(1) model outperforms all the other models in predicting inflation. We also conduct ex ante forecasts from the BVAR and AR(1) models over 2010:Q1-2012:Q4, to highlight their ability to track turning points in output and inflation respectively.

Keywords: South Africa; global vector autoregressive (GVAR) model; Bayesian vector autoregressive (BVAR) model; forecasting (search for similar items in EconPapers)
JEL-codes: C51 C53 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2013-08
New Economics Papers: this item is included in nep-afr and nep-for
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Journal Article: Do we need a global VAR model to forecast inflation and output in South Africa? (2015) Downloads
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