Abstract:
This study investigates the asymmetric and time-varying causalities between
inflation and inflation uncertainty in South Africa within a conditional
Gaussian Markov switching vector autoregressive (MS-VAR)
model framework. The MS-VAR model is capable of determining both
the sign and direction of causality. We account for the nonlinear, long
memory and seasonal features of the inflation series simultaneously
by measuring inflation uncertainty as the conditional variance of inflation
generated by recursive estimation of a Seasonal Fractionally Integrated
Smooth Transition Autoregressive Asymmetric Power GARCH
(SEA-FISTAR-APGARCH) model using monthly data for the period
1921:01 to 2012:12. The recursive, rather than full-sample, estimation
allows us to obtain a time-varying measure of uncertainty and better
mimics the real-time scenario faced by economic agents and/or policy
makers. The inferred probabilities from the four-state MS-VAR model
show evidence of a time-varying relationship. The conditional (i.e.
lead–lag) and regime-prediction Granger causality provide evidence in
favor of Friedman's hypothesis. This implies that past information on inflation
can help improve the one-step-ahead prediction of inflation uncertainty
but not vice versa. Our results have some important policy
implications.
