The performance of Bayesian VAR Markov switching and logistic regression models with Monte Carlo simulated data

Abstract

In this study, the main intention is to build an early warning system (EWS) model for inflation in South Africa using the findings from the Markov-switching Bayesian vector autoregressive (MSBVAR) on logistic regression model. Monte Carlo experimental methods are used to simulate both the inflation rate and repo rate of South Africa. In total, the procedure simulated 210 observations for the period January 1999 to June 2016. For this data generating process, the study followed a Gibbs sampling technique. Prior to model estimation, preliminary test of nonlinearity called the Brock Dechert Scheinkman (BDS) test was employed and the results confirmed the data to be nonlinear and suitable for MS-BVAR method. The Kapetanios-Shin-Snell nonlinear augmented Dickey-Fuller (KSS-NADF) also confirmed the presence of nonlinear unit root in the simulated series. Moreover, the RESET test, CUSUM and Bai Perron multiple break point tests were also calculated to determine if there is structural change in the data and that the model is correctly specified. With the attempt to build an early warning system (EWS) model, the study estimates the MS − BVAR(1) model of two regime shifts. This model serves as a primary tool in detecting regime shifts in inflation in terms of low and high regimes. The results of the MS(2) − BVAR(1) indicates that the SA inflation might be in low inflation regime for the period of 11 years and 4 months. Furthermore, the results of the logistic regression revealed that the repo rate is not a good tool to predict inflation rate. The results of the marginal effects of the repo rate towards inflation rate implied that if everything held constant, a 1% increase in repo in a month increases inflation by 81%. Similar results were also reported by several authors such as Mboweni et al. (2008); Gupta and Komen (2009); and Bonga-Bonga and Kabundi (2015). In predicting the possibility of inflation crisis in SA, the assessment of the EWS model confirmed that only 57% of the inflation crises are correctly called for by the in-sample model compared to the 45% of correctly called by out-ofsample model forecasts. The study concluded that combating inflation rates in South Africa (SA) using variables such as repo rate might not be a good idea as this might also increase the likelihood of SA being be into inflationary. Finally, the study recommends the enhancement of error correction model to the MSBVAR model when including other determinants of inflation rate in the analysis. The study might provide a clearer picture about both the long-term and short-term relationships between inflation and related variables. Such findings might be used by policy makers to embark on strategies to combat the anticipated inflationary crisis in South Africa.