M.Com. (Econometrics) / An attempt has been made in this study to model electricity sales of Eskom with the aid of an econometric model. The study examines the history of econometric modeling and the applications thereof. These applications include forecasting, policy simulation and policy analysis. The sales of electricity within the specific sales categories is estimated and simulated with the use of an econometric model. The model is specified according to the expected dependent and explanatory variables. The a priori theoretical considerations concerning the size and sign of the parameters of the function are also included in the specification, as well as the mathematical form of the model. The specification process of the econometric model is based on econometric theory and on available information relating to the phenomenon being studied. The method of ordinary least squares is used in the estimation of the parameters of the model. As this is an econometric study, the emphasis is on the evaluation of the results. Economic a priori criteria, statistical criteria and econometric criteria are used to evaluate the results of the parameters obtained by the method of ordinary least squares. Several tests, including the Goldfeld Quandt test for heteroscedasticity, the Durbin-Watson test for auto correlation and the Frisch analysis of multicollinearity are executed. The overall results of the tests to which the model was subjected, was satisfactory. The best functions are combined in a structured model. This model is simulated with the use of the Gauss-Seidel-method and forecasts of historical values are obtained. Statistical tests for the validation of these results, as well as Theil's inequality coefficient are applied to test the forecasting power of the model. The results of the ex post forecast for the period 1988.1 to 1995.1 emphasize the usefulness of the model as a forecasting device and the dynamic simulation demonstrates the ability of the model to reproduce the historical data from which it is estimated.

D. Comm. / The South African vehicle industry is currently struggling to narrow the gap between the demand and supply for new vehicles. Customers are confronted with long waiting lists for some models, while producers are carrying high stock levels on other models. A possible explanation for this is the complex nature of the demand for vehicles, resulting in difficulty to analyze and forecast vehicle sales. The demand for large and luxury passenger vehicles is to a. large extent status-driven, while the demand for commercial vehicles and tractors can be seen as derived demand because they are being used in the production process. Motorcycles, on the other hand, can be seen as "inferior goods" due to their discomfort. Demand management suggests that managers must react proactively to changes in the market with the aid of strategic information systems. A key ingredient of information systems is econometric models. These models transform data into decision-relevant information. The availability and knowledge of these models are, however, very limited. Studies performed on the vehicle industry produced only a few broadly defined models. They analyze only the main categories and do not, for example, distinguish between small and large vehicles, while this mix of sales is important for the majority of stakeholders in the vehicle industry. If forecasts are made with these models, decisions will be based on inaccurate forecasts and scarce resources will be wasted. This study is executed against this background. It is an attempt to narrow this gap between demand and supply and to address the shortcomings of previous econometric models. The primary objective of the study is to compile and test an econometric model for each vehicle category in South Africa. The secondary objective is to investigate the use of econometric models in the strategic planning processes in the vehicle industry to gain a competitive advantage. In order to make conclusions and recommendations to the industry, the following steps were followed: All vehicle categories in South Africa were identified (small passenger cars, medium passenger cars, large passenger cars, light commercial vehicles, medium commercial vehicles, heavy commercial vehicles, tractors and motorcycles). A unique data base was compiled for each category of vehicle (prices of new vehicles, prices of second-hand vehicles and numbers sold). Economic and graphic analyses were performed on every category, investigating the determinants of the demand for that category of vehicle. An econometric model was estimated for each category of vehicle. These models were tested economically, statistically and econometrically to verify the soundness thereof. The uses of these models were illustrated ("what if" analyses and forecasting). The role of managers in the vehicle industry in the application of these models were investigated. It can be concluded from the results of the econometric models that consumer behaviour in the vehicle industry can be analysed and forecast quite accurately. It was proven from the results that the factors influencing the demand for the different categories of vehicles, and especially the extent to which these determinants influence demand, differ considerably among the categories. This emphasizes the importance of analyses of this nature where the determinants of demand are analyzed for each and every type of vehicle. This also emphasizes the risk managers take when decisions are based on models where all these categories are combined and only total vehicle demand is analyzed. The responsiveness of management depends to a large extent on the quality of the information systems in the company. The new approach, 'identified in this study, concentrates on the direct use of information systems, and more specifically econometric models, to establish a competitive advantage.

Historically, the Consumption Capital Asset Pricing Method (C-CAPM) has performed poorly in that estimated parameters are implausible, model restrictions are often rejected and inferences appear to be very sensitive to the choice of economic agents' preferences. In this study, we estimate and test the C-CAPM with Constant Relative Risk Aversion (CRRA) using time series data from Germany, South Africa, Britain and America during relatively short time periods with the latest available data sets. Hansen's GMM approach is applied to estimate the parameters arising from this model. In general, estimated parameters fall outside the bounds specified by Lund & Engsted (1996) and Cuthbertson & Nitzsche (2004), even though the models are not rejected by the J-test and are associated with relatively small minimum distances.