DYNAMIC TIME INCONSISTENCY AND THE SOUTH AFRICAN RESERVE BANK

This paper derives the econometric restrictions imposed by the Barro and Gordon model of dynamic time inconsistency on a bivariate time-series model of consumer price index (CPI) inflation and real gross domestic product (GDP), and tests these restrictions based on quarterly data for South Africa covering the period of January 1960-April 1999, i.e. for the pre-inflation targeting period. The results show that the data are consistent with the short- and long-run implications of the theory of time-consistent monetary policy. Moreover, when the model is used to forecast one-step-ahead inflation over the period of January 2001-February 2008, i.e. the period covering the starting point of the inflation-targeting regime until date, we, on average, obtain lower rates of inflation. The result tends to suggest that the South African Reserve Bank perhaps needs to manage the inflation-targeting framework better than it has done so far.     

A BVAR MODEL FOR THE SOUTH AFRICAN ECONOMY

The paper develops a Bayesian vector autoregressive (BVAR) model of the South African economy for the period of 1970:1‐2000:4 and forecasts GDP, consumption, investment, short‐term and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the BVAR model is compared with the same generated from the univariate and unrestricted VAR models. The BVAR model is found to produce the most accurate out of sample forecasts. The same is also capable of correctly predicting the direction of change in the chosen macroeconomic indicators.     

A SMALL-SCALE DSGE MODEL FOR FORECASTING THE SOUTH AFRICAN ECONOMY

This paper uses a version of Hansen's (1985) Dynamic Stochastic General Equilibrium (DSGE) model to forecast the South African economy. The calibrated model, based on annual data over the period of 1970‐2000, is used to generate one‐ to eight‐quarters‐ahead out‐of‐sample forecast errors for the period of 2001:1 to 2005:4. The forecast errors are then compared with the unrestricted versions of the Classical and Bayesian VARs. A Bayesian VAR with relatively loose priors outperforms both the classical VAR and the DSGE model.     

FORECASTING THE SOUTH AFRICAN ECONOMY WITH GIBBS SAMPLED BVECMs

The paper uses the Gibbs sampling technique to estimate a heteroscedastic Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1‐2000:4, and then forecasts GDP, consumption, investment, short and long term interest rates, and the CPI over the period of 2001:1 to 2005:4. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the Gibbs sampled BVECM is compared with those generated from a Classical VECM and a homoscedastic BVECM. The homoscedastic BVECM is found to produce the most accurate out of sample forecasts.     

FORECASTING THE SOUTH AFRICAN ECONOMY WITH VARs AND VECMs

The paper develops a Bayesian Vector Error Correction Model (BVECM) of the South African economy for the period 1970:1‐2000:4 and forecasts GDP, consumption, investment, short and long term interest rates, and the CPI. We find that a tight prior produces relatively more accurate forecasts than a loose one. The out‐of‐sample‐forecast accuracy resulting from the BVECM is compared with those generated from the Classical variant of the VAR and VECM and the Bayesian VAR. The BVECM is found to produce the most accurate out of sample forecasts. It also correctly predicts the direction of change in the chosen macroeconomic indicators.     

BAYESIAN METHODS OF FORECASTING INVENTORY INVESTMENT

This paper develops a Bayesian Vector Error Correction Model (BVECM) for forecasting inventory investment. The model is estimated using South African quarterly data on actual sales, production, unfilled orders, price level and interest rate, for the period 1978 to 2000. The out-of-sample-forecast accuracy obtained from the BVECM over the forecasting horizon of 2001:1 to 2003:4, is compared with those generated from the classical variant of the Vector Autoregresssive (VAR) model and the VECM, the Bayesian VAR, and the recently developed ECM by Smith et al. , for the South African economy. The BVECM with the most-tight prior outperforms all the other models, except for a relatively tight BVAR which also correctly predicts the direction of change of inventory investment over the period of 2004:1 to 2006:3.     

TESTING FOR PPP USING SADC REAL EXCHANGE RATES

This paper attempts to provide evidence indicating that the purchasing power parity (PPP) puzzle is becoming less of a puzzle. It present the results of Augmented Dickey–Fuller test, non-linear tests of non-stationarity and Bayesian unit root tests, applied to 10 Southern African Development Community countries. The Bayesian tests were found to be biased in favour of a trend stationary model in all cases. It is argued that non-linear approaches to exchange rate adjustments are likely to provide a firmer basis for inference and stronger support for the PPP in the long-term. This is more so at 1 and 5% levels of significance.     

SPATIAL BAYESIAN METHODS OF FORECASTING HOUSE PRICES IN SIX METROPOLITAN AREAS OF SOUTH AFRICA

This paper estimates Spatial Bayesian Vector Autoregressive (SBVAR) models, based on the First-Order Spatial Contiguity and the Random Walk Averaging priors, for six metropolitan areas of South Africa, using monthly data over the period of 1993:07 to 2005:06. We then forecast one- to six-months-ahead house prices over the forecast horizon of 2005:07 to 2007:06. When we compare forecasts generated from the SBVARs with those from an unrestricted Vector Autoregressive (VAR) and the Bayesian Vector Autoregressive (BVAR) models based on the Minnesota prior, we find that the spatial models tend to outperform the other models for large middle-segment houses; while the VAR and the BVAR models tend to produce lower average out-of-sample forecast errors for middle and small-middle segment houses, respectively. In addition, based on the priors used to estimate the Bayesian models, our results also suggest that prices tend to converge for both large- and middle-sized houses, but no such evidence could be obtained for the small-sized houses.     

MEASURING THE WELFARE COST OF INFLATION IN SOUTH AFRICA

In this paper, we estimate the long‐run equilibrium relationship between money balance as a ratio of income and the Treasury bill rate for the period of 1965:02 to 2007:01, and in turn use the relationship to obtain welfare cost estimates of inflation. Using the Johansen technique, we estimate a log‐log specification and a semi‐log model of the above relationship. Based on the fits of the specifications, we decided to rely more on the welfare cost measure obtained under the log‐log money demand model. Our estimates suggest that the welfare cost of inflation for South Africa ranges between 0.34% and 0.67% of GDP, for a band of 3‐6% of inflation. Thus, it seems that the South African Reserve Bank's current inflation target band of 3‐6% is not too poorly designed in terms of welfare.