Forecasting Key Macroeconomic Variables of the South African Economy using Bayesian Variable Selection

Abstract

We compare the forecasting performances of the classical and the Minnesota-type Bayesian vector autoregressive (VAR) models with those of linear (fixed-parameter) and nonlinear (time-varying parameter) VARs involving a stochastic search algorithm for variable selection, estimated using Markov Chain Monte Carlo methods. In this regard, we analyze the forecasting performances of all these models in predicting one- to eight-quarters-ahead of the growth rate of GDP, the consumer price index inflation rate and the three months Treasury bill rate for South Africa over an out-of-sample period of 2000:Q1-2011:Q2, using an in-sample period of 1960:Q1-1999:Q4. In general, we find that variable selection, whether imposed on a time-varying VAR or a fixed parameter VAR, and non-linearity in VARs play an important part in improving predictions when compared to the linear fixed coefficients classical VAR. However, we do not observe marked gains in forecasting power across the different Bayesian models, as well as, over the classical VAR model, possibly because the problem of over parameterization in the classical VAR is not that acute in our three-variable system.