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.     

FORECASTING PERFORMANCE OF AN ESTIMATED DSGE MODEL FOR THE SOUTH AFRICAN ECONOMY

We construct a small open‐economy New Keynesian dynamic stochastic general equilibrium (DSGE) model for South Africa with nominal rigidities, incomplete international risk sharing and partial exchange rate pass‐through. The parameters of the model are estimated using Bayesian methods, and its out‐of‐sample forecasting performance is compared with Bayesian vector autoregression (VAR), classical VAR and random‐walk models. Our results indicate that the DSGE model generates forecasts that are competitive with those from other models, and it contributes statistically significant information to combined forecast measures.     

AN OPEN ECONOMY NEW KEYNESIAN DSGE MODEL OF THE SOUTH AFRICAN ECONOMY

In this paper an open economy New Keynesian model of the South African economy is presented. The model is constructed to provide for incomplete pass-through of exchange rate changes, external habit formation, partial indexation of domestic prices and wages to past inflation, and staggered price and wage setting. Furthermore, the model is estimated using Bayesian techniques on South African domestic and trade partner data for the period 1990Q1 to 2007Q4. The estimated model is analysed by means of impulse response functions.     

THE SOUTH AFRICAN PHILLIPS CURVE: HOW APPLICABLE IS THE GORDON MODEL?

Is there a Phillips curve relationship present in South Africa and if so, what form does it take? Traditionally the method to establish whether or not there is a relationship between the output gap and the change in inflation is merely to regress the latter on the former. This yields the well‐known augmented Phillips curve. However, Gordon has argued that this specification of the Phillips curve produces biased results. Instead, he puts forward and estimates successfully for several industrialised countries his so‐called triangular model that tests for hysteresis and inertia in the behaviour of inflation, as well as the impact on inflation of changes in the output level. This paper considers whether or not Gordon's triangle model is applicable to South Africa, i.e. are hysteresis and inertia present in South Africa? In addition, in an attempt to find a better estimation of the output gap, the paper also experiments with alternative ways to estimate the long‐run output level, including the standard HP‐filter, as well as a production function approach.     

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.     

THE ROLE OF THE EXCHANGE RATE IN A NEW KEYNESIAN DSGE MODEL FOR THE SOUTH AFRICAN ECONOMY

We build a small open economy New Keynesian dynamic stochastic general equilibrium model for South Africa similar to Steinbach et al. We abandon their assumption of complete risk sharing with the foreign economy, and introduce country risk shocks to allow deviations from uncovered interest rate parity. These changes allow us to include the exchange rate as an observable variable in the estimation of the model. Using forecast error variance decompositions and historical decompositions, we show that country risk shocks have sizable effects on the South African business cycle. We also explore the optimal monetary policy implications of our model within the context of Taylor rules.     

THE PROPERTIES OF CYCLES IN SOUTH AFRICAN FINANCIAL VARIABLES AND THEIR RELATION TO THE BUSINESS CYCLE

The paper describes cyclicality in a range of local and international financial variables and their relation to cyclical behaviour in the South African real economy. Cycles are derived using a dating algorithm similar to that used to determine business cycle turning points and falls within the Burns‐Mitchell tradition of business cycle analysis. Co‐movement between phases in financial variables and similar phases in the business cycle are described using the concordance statistic, instead of the correlation statistic (which requires stationarity). This is a preliminary step in identifying financial variables that can act as leading indicators of economic activity.     

THE CHANGING VOLATILITY OF THE SOUTH AFRICAN ECONOMY

During the last decade economic literature explored the presence of and reasons for what became known as “the great moderation” in the US and other G7 countries. “The great moderation” describes the decrease in economic volatility experienced in many of the G7 countries. This paper finds that in South Africa volatility is also not constant (it even finds that there are autoregressive conditional heteroskedastic effects present) and that volatility also decreased, particularly since 1994. Following the literature, the paper explores several reasons for this decrease and finds that smaller shocks, better monetary policy and improvements in the financial sector that place less liquidity constraints on individuals and allow them to manage their debt better are some of the main reasons for the reduction in the volatility of the South African economy. The literature on the G7 also suggests that better inventory management contributed to the lower volatility. However, this seems not to be true for South Africa.     

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.     

REVISITING SOUTH AFRICAN EMPLOYMENT TRENDS IN THE 1990S

This article revisits South African employment trends recorded since 1995. In particular, it investigates whether the job losses and gains recorded by the October Household Survey jobs in the mid-1990s reflect the reality. This is done by comparing the different official data sets, and by exploring alternative sources of information for three sectors that substantially influenced this trend, namely formal agriculture, mining, and community, social and personal services. Potential inconsistencies within the October Household data are assessed, particularly in relation to the distribution of employees across formal and informal sectors and the categorisation of unpaid family workers. The implications of possible changes to the employment trend from 1995-2006 are considered. This article finds that the evidence is strong enough to call into question published employment trends. According to the October Household Survey, formal employment fell by 1.4 million between 1995 and 1997. The OHS and Labour Force Survey shows that formal employment then grew by 1.9 million between 1997 and 2006. According to the revised figures presented in this paper, 73,000 to 530,000 formal jobs were lost between 1995 and 1997 and 1.4 million net new jobs were created between 1997 and 2006. It is therefore possible that the plummeting and "recovery" of employment in the 1990s were both considerably less dramatic than that reflected in the official statistics. Further research and investigation would be required to validate these trends.     

FORECASTING RECESSION IN SOUTH AFRICA: A COMPARISON OF THE YIELD CURVE AND OTHER ECONOMIC INDICATORS

The paper uses the standard probit model proposed by Estrella and Mishkin (1996) , as well as the modified probit model suggested by Dueker (1997) , to examine the ability of the yield curve to predict recessions in South Africa, and compares its predictive power with other commonly used variables such as the growth rate of real money supply, changes in stock prices and the index of leading economic indicators. Compared with other indicators, real M3 growth does not provide much information about future recessions, whilst movements in the All‐Share index provide information for up to 12 months but do not do better than the yield curve. The index of leading economic indicators outperforms the yield spread in the short run up to 4 months but the yield spread performs better at longer horizons.     

ISOLATING A MEASURE OF INFLATION EXPECTATIONS FOR THE SOUTH AFRICAN FINANCIAL MARKET USING FORWARD INTEREST RATES

The inflation expectations channel of the transmission mechanism is generally recognised as crucial for the implementation of modern monetary policy. This paper briefly reviews the practices commonly employed for measuring inflation expectations in South Africa, and offers an additional method, which is market based. The methodologies of Nelson and Siegel and Svensson are applied to determine implied nominal and real forward interest rates. The difference between the nominal and real forward rates (called inflation compensation) on a particular day is then used as a proxy for the market's inflation expectations. This measure should not be viewed as a substitute for other measures of inflation expectations, but should rather supplement these in order to offer an additional insight.