Ekonomiese streekmodellering, met spesiale verwysing na streek H
- Authors: De Wet, Melanie
- Date: 2014-10-29
- Subjects: Econometric models , Economic development - Econometric models
- Type: Thesis
- Identifier: http://ujcontent.uj.ac.za8080/10210/381271 , uj:12704 , http://hdl.handle.net/10210/12569
- Description: D.Com. (Econometrics) , The main aim with the thesis was to outline the use of regional econometric modelling as a technique for the modelling of the interdependency in the development regions of South Africa. In particular, an regional econometric model of Region H was constructed. There is no doubt that modelling is here to stay - as part of the analytical process used in scholarly studies and in applications, especially for making policy in both the public and privatedomain. Although macro-econometric modelling has been withus for sometime, the art of building large-scale· regional economic models is relatively new, especially in South Africa where such models have never been in use. The problem of forecasting regional economic activity has become an important component of regional research. The most frequently used forecasting techniques have been input-output model. A regional econometric model can be defined as a set of equations, sometimes highly simultaneous, describing the economic structure of a regional economy. The parameters of the equations are estimated economically largely by regression equations.
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- Authors: De Wet, Melanie
- Date: 2014-10-29
- Subjects: Econometric models , Economic development - Econometric models
- Type: Thesis
- Identifier: http://ujcontent.uj.ac.za8080/10210/381271 , uj:12704 , http://hdl.handle.net/10210/12569
- Description: D.Com. (Econometrics) , The main aim with the thesis was to outline the use of regional econometric modelling as a technique for the modelling of the interdependency in the development regions of South Africa. In particular, an regional econometric model of Region H was constructed. There is no doubt that modelling is here to stay - as part of the analytical process used in scholarly studies and in applications, especially for making policy in both the public and privatedomain. Although macro-econometric modelling has been withus for sometime, the art of building large-scale· regional economic models is relatively new, especially in South Africa where such models have never been in use. The problem of forecasting regional economic activity has become an important component of regional research. The most frequently used forecasting techniques have been input-output model. A regional econometric model can be defined as a set of equations, sometimes highly simultaneous, describing the economic structure of a regional economy. The parameters of the equations are estimated economically largely by regression equations.
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Die toepasbaarheid van die Monte Carlo studies op empiriese data van die Suid-Afrikaanse ekonomie
- Authors: McClintock, Michael
- Date: 2014-07-29
- Subjects: Econometric models , Monte Carlo method
- Type: Thesis
- Identifier: uj:11917 , http://hdl.handle.net/10210/11645
- Description: M.Com.(Econometrics) , The objective of this study is to evaluate different estimation techniques that can be used to estimate the coefficients of a model. The estimation techniques were applied to empirical data drawn from the South African economy. The Monte Carlo studies are unique in that data was statistically generated for the experiments. This approach was due to the fact that actual observations on economic variables contain several econometric problems, such as autocorrelation and MUlticollinearity, simultaneously. However, the approach in this study differs in that empirical data is used to evaluate the estimation techniques. The estimation techniques evaluated are : • Ordinary least squares method • Two stage least squares method • Limited information maximum likelihood method • Three stage least squares method • Full information maximum likelihood method. The estimates of the different coefficients are evaluated on the following criteria : • The bias of the estimates • The variance of the estimates • t-values of the estimates • The root mean square error. The ranking of the estimation techniques on the bias criterion is as follows : 1 Full information maximum likelihood method. 2 Ordinary least squares method 3 Three stage least squares method 4 Two stage least squares method 5 Limited information maximum likelihood method The ranking of the estimation techniques on the variance criterion is as follows : 1 Full information maximum likelihood method. 2 Ordinary least squares method 3 Three stage least squares method 4 Two stage least squares method 5 Limited information maximum.likelihood method All the estimation techniques performed poorly with regard to the statistical significance of the estimates. The ranking of the estimation techniques on the t-values of the estimates is thus as follows 1 Three stage least squares method 2 ordinary least squares method 3 Two stage least squares method and the limited information maximum likelihood method 4 Full information maximum likelihood method. The ranking of the estimation techniques on the root mean square error criterion is as follows : 1 Full information maximum likelihood method and the ordinary least squares method 2 Two stage least squares method 3 Limited information maximum likelihood method and the three stage least squares method The results achieved in this study are very similar to those of the Monte Carlo studies. The only exception is the ordinary least squares method that performed better on every criteria dealt with in this study. Though the full information maximum likelihood method performed the best on two of the criteria, its performance was extremely poor on the t-value criterion. The ordinary least squares method is shown, in this study, to be the most constant performer.
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- Authors: McClintock, Michael
- Date: 2014-07-29
- Subjects: Econometric models , Monte Carlo method
- Type: Thesis
- Identifier: uj:11917 , http://hdl.handle.net/10210/11645
- Description: M.Com.(Econometrics) , The objective of this study is to evaluate different estimation techniques that can be used to estimate the coefficients of a model. The estimation techniques were applied to empirical data drawn from the South African economy. The Monte Carlo studies are unique in that data was statistically generated for the experiments. This approach was due to the fact that actual observations on economic variables contain several econometric problems, such as autocorrelation and MUlticollinearity, simultaneously. However, the approach in this study differs in that empirical data is used to evaluate the estimation techniques. The estimation techniques evaluated are : • Ordinary least squares method • Two stage least squares method • Limited information maximum likelihood method • Three stage least squares method • Full information maximum likelihood method. The estimates of the different coefficients are evaluated on the following criteria : • The bias of the estimates • The variance of the estimates • t-values of the estimates • The root mean square error. The ranking of the estimation techniques on the bias criterion is as follows : 1 Full information maximum likelihood method. 2 Ordinary least squares method 3 Three stage least squares method 4 Two stage least squares method 5 Limited information maximum likelihood method The ranking of the estimation techniques on the variance criterion is as follows : 1 Full information maximum likelihood method. 2 Ordinary least squares method 3 Three stage least squares method 4 Two stage least squares method 5 Limited information maximum.likelihood method All the estimation techniques performed poorly with regard to the statistical significance of the estimates. The ranking of the estimation techniques on the t-values of the estimates is thus as follows 1 Three stage least squares method 2 ordinary least squares method 3 Two stage least squares method and the limited information maximum likelihood method 4 Full information maximum likelihood method. The ranking of the estimation techniques on the root mean square error criterion is as follows : 1 Full information maximum likelihood method and the ordinary least squares method 2 Two stage least squares method 3 Limited information maximum likelihood method and the three stage least squares method The results achieved in this study are very similar to those of the Monte Carlo studies. The only exception is the ordinary least squares method that performed better on every criteria dealt with in this study. Though the full information maximum likelihood method performed the best on two of the criteria, its performance was extremely poor on the t-value criterion. The ordinary least squares method is shown, in this study, to be the most constant performer.
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Die ekonometriese modellering van die Suid-Afrikaanse monetêre stelsel
- Authors: De Wet, Melanie
- Date: 2014-04-14
- Subjects: Econometric models , Monetary policy - South Africa
- Type: Thesis
- Identifier: uj:10621 , http://hdl.handle.net/10210/10142
- Description: M.Comm. (Econometrics) , Please refer to full text to view abstract
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- Authors: De Wet, Melanie
- Date: 2014-04-14
- Subjects: Econometric models , Monetary policy - South Africa
- Type: Thesis
- Identifier: uj:10621 , http://hdl.handle.net/10210/10142
- Description: M.Comm. (Econometrics) , Please refer to full text to view abstract
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Eenvoudige ekonometriese vooruitskattingsmodelle vir geselekteerde invoergoedere deur Suid-Afrika se seehawens vir die periode 1982 tot 1994
- Authors: Engelbrecht, Josias Andreas
- Date: 2014-07-21
- Subjects: Econometric models , Import quotas - Mathematical models
- Type: Thesis
- Identifier: uj:11721 , http://hdl.handle.net/10210/11446
- Description: M.Com. (Economics) , Please refer to full text to view abstract
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- Authors: Engelbrecht, Josias Andreas
- Date: 2014-07-21
- Subjects: Econometric models , Import quotas - Mathematical models
- Type: Thesis
- Identifier: uj:11721 , http://hdl.handle.net/10210/11446
- Description: M.Com. (Economics) , Please refer to full text to view abstract
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Die kombinering van vooruitskattings : 'n toepassing op die vernaamste makro-ekonomiese veranderlikes
- Authors: Ruthven, Christelle
- Date: 2014-02-18
- Subjects: Econometrics , Econometric models , Economic forecasting - Econometric models
- Type: Thesis
- Identifier: uj:4091 , http://hdl.handle.net/10210/9439
- Description: M.Com. (Econometrics) , The main purpose of this study is the combining of forecasts with special reference to major macroeconomic series of South Africa. The study is based on econometric principles and makes use of three macro-economic variables, forecasted with four forecasting techniques. The macroeconomic variables which have been selected are the consumer price index, consumer expenditure on durable and semi-durable products and real M3 money supply. Forecasts of these variables have been generated by applying the Box-Jenkins ARIMA technique, Holt's two parameter exponential smoothing, the regression approach and mUltiplicative decomposition. Subsequently, the results of each individual forecast are combined in order to determine if forecasting errors can be minimized. Traditionally, forecasting involves the identification and application of the best forecasting model. However, in the search for this unique model, it often happens that some important independent information contained in one of the other models, is discarded. To prevent this from happening, researchers have investigated the idea of combining forecasts. A number of researchers used the results from different techniques as inputs into the combination of forecasts. In spite of the differences in their conclusions, three basic principles have been identified in the combination of forecasts, namely: i The considered forecasts should represent the widest range of forecasting techniques possible. Inferior forecasts should be identified. Predictable errors should be modelled and incorporated into a new forecast series. Finally, a method of combining the selected forecasts needs to be chosen. The best way of selecting a m ethod is probably by experimenting to find the best fit over the historical data. Having generated individual forecasts, these are combined by considering the specifications of the three combination methods. The first combination method is the combination of forecasts via weighted averages. The use of weighted averages to combine forecasts allows consideration of the relative accuracy of the individual methods and of the covariances of forecast errors among the methods. Secondly, the combination of exponential smoothing and Box-Jenkins is considered. Past errors of each of the original forecasts are used to determine the weights to attach to the two original forecasts in forming the combined forecasts. Finally, the regression approach is used to combine individual forecasts. Granger en Ramanathan (1984) have shown that weights can be obtained by regressing actual values of the variables of interest on the individual forecasts, without including a constant and with the restriction that weights add up to one. The performance of combination relative to the individual forecasts have been tested, given that the efficiency criterion is the minimization of the mean square errors. The results of both the individual and the combined forecasting methods are acceptable. Although some of the methods prove to be more accurate than others, the conclusion can be made that reliable forecasts are generated by individual and combined forecasting methods. It is up to the researcher to decide whether he wants to use an individual or combined method since the difference, if any, in the root mean square percentage errors (RMSPE) are insignificantly small.
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- Authors: Ruthven, Christelle
- Date: 2014-02-18
- Subjects: Econometrics , Econometric models , Economic forecasting - Econometric models
- Type: Thesis
- Identifier: uj:4091 , http://hdl.handle.net/10210/9439
- Description: M.Com. (Econometrics) , The main purpose of this study is the combining of forecasts with special reference to major macroeconomic series of South Africa. The study is based on econometric principles and makes use of three macro-economic variables, forecasted with four forecasting techniques. The macroeconomic variables which have been selected are the consumer price index, consumer expenditure on durable and semi-durable products and real M3 money supply. Forecasts of these variables have been generated by applying the Box-Jenkins ARIMA technique, Holt's two parameter exponential smoothing, the regression approach and mUltiplicative decomposition. Subsequently, the results of each individual forecast are combined in order to determine if forecasting errors can be minimized. Traditionally, forecasting involves the identification and application of the best forecasting model. However, in the search for this unique model, it often happens that some important independent information contained in one of the other models, is discarded. To prevent this from happening, researchers have investigated the idea of combining forecasts. A number of researchers used the results from different techniques as inputs into the combination of forecasts. In spite of the differences in their conclusions, three basic principles have been identified in the combination of forecasts, namely: i The considered forecasts should represent the widest range of forecasting techniques possible. Inferior forecasts should be identified. Predictable errors should be modelled and incorporated into a new forecast series. Finally, a method of combining the selected forecasts needs to be chosen. The best way of selecting a m ethod is probably by experimenting to find the best fit over the historical data. Having generated individual forecasts, these are combined by considering the specifications of the three combination methods. The first combination method is the combination of forecasts via weighted averages. The use of weighted averages to combine forecasts allows consideration of the relative accuracy of the individual methods and of the covariances of forecast errors among the methods. Secondly, the combination of exponential smoothing and Box-Jenkins is considered. Past errors of each of the original forecasts are used to determine the weights to attach to the two original forecasts in forming the combined forecasts. Finally, the regression approach is used to combine individual forecasts. Granger en Ramanathan (1984) have shown that weights can be obtained by regressing actual values of the variables of interest on the individual forecasts, without including a constant and with the restriction that weights add up to one. The performance of combination relative to the individual forecasts have been tested, given that the efficiency criterion is the minimization of the mean square errors. The results of both the individual and the combined forecasting methods are acceptable. Although some of the methods prove to be more accurate than others, the conclusion can be made that reliable forecasts are generated by individual and combined forecasting methods. It is up to the researcher to decide whether he wants to use an individual or combined method since the difference, if any, in the root mean square percentage errors (RMSPE) are insignificantly small.
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Die ekonometriese verbetering van die stochastiese vergelykings van 'n ekonometriese model : met spesifieke vermelding van stasionariteit en ko-integrasie
- Authors: Naude, Yolanda
- Date: 2012-08-22
- Subjects: Econometrics , Econometric models
- Type: Thesis
- Identifier: uj:3003 , http://hdl.handle.net/10210/6426
- Description: M.Comm. , The aim of this study is the econometric improvement of the stochastic equations of an econometric model with specific reference made to the explanation and incorporation of stationarity and cointegration testing. The study is based on an existing macroeconometric forecasting model. The focus of the study is not on the improvement of the specification of individual equations per se, but rather on the econometric improvement thereof, therefore changes to the specification of individual equations have only been made in cases where test results strongly recommended it. The RAU-model had previously been exposed to neither structural stability-, stationarity-, nor cointegration testing and therefore both the explanation and implementation of these tests have been included in the study. It is, however, important to note that the main purpose of both stationarity and co-integration testing is not to substitute nonstationary data with data which is proven to be stationary, but rather to identify nonstationary and non-cointegrationary data for future improvement and enhancement of the RAU model. Following the completion of the abovementioned tests, parameters have been estimated for the individual equations of the three sectors of the RAU-model (i.e. the Real-, Balance of payments-, and the Monetary sectors). Thereafter the results have been evaluated on the basis of the economic-, statistic-, and econometric evaluation criteria. In cases where econometric inconsistencies arose from the violation of the assumptions underlying the econometric tests, appropriate transformation processes have been applied in an attempt to resolve the problem. Thereafter, tests have been carried out to determine the forecasting ability of the model as well as to compare the model results with the a priori results. In general, the aim of the study, to econometrically improve the stochastic equations of the RAU model, has been achieved on the basis of overall better regression- and evaluation results that have been obtained. Following the completion of the study, a new approach to econometric modelbuilding, which makes provision for the inclusion of both stationarity- and cointegration testing, is proposed.
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- Authors: Naude, Yolanda
- Date: 2012-08-22
- Subjects: Econometrics , Econometric models
- Type: Thesis
- Identifier: uj:3003 , http://hdl.handle.net/10210/6426
- Description: M.Comm. , The aim of this study is the econometric improvement of the stochastic equations of an econometric model with specific reference made to the explanation and incorporation of stationarity and cointegration testing. The study is based on an existing macroeconometric forecasting model. The focus of the study is not on the improvement of the specification of individual equations per se, but rather on the econometric improvement thereof, therefore changes to the specification of individual equations have only been made in cases where test results strongly recommended it. The RAU-model had previously been exposed to neither structural stability-, stationarity-, nor cointegration testing and therefore both the explanation and implementation of these tests have been included in the study. It is, however, important to note that the main purpose of both stationarity and co-integration testing is not to substitute nonstationary data with data which is proven to be stationary, but rather to identify nonstationary and non-cointegrationary data for future improvement and enhancement of the RAU model. Following the completion of the abovementioned tests, parameters have been estimated for the individual equations of the three sectors of the RAU-model (i.e. the Real-, Balance of payments-, and the Monetary sectors). Thereafter the results have been evaluated on the basis of the economic-, statistic-, and econometric evaluation criteria. In cases where econometric inconsistencies arose from the violation of the assumptions underlying the econometric tests, appropriate transformation processes have been applied in an attempt to resolve the problem. Thereafter, tests have been carried out to determine the forecasting ability of the model as well as to compare the model results with the a priori results. In general, the aim of the study, to econometrically improve the stochastic equations of the RAU model, has been achieved on the basis of overall better regression- and evaluation results that have been obtained. Following the completion of the study, a new approach to econometric modelbuilding, which makes provision for the inclusion of both stationarity- and cointegration testing, is proposed.
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Die insluiting van besigheidsverwagtingsdata in ekonometriese modelle : die Suid-Afrika geval
- Authors: Van der Merwe, Ronell
- Date: 2015-02-09
- Subjects: Econometric models , Economics - South Africa
- Type: Thesis
- Identifier: uj:13234 , http://hdl.handle.net/10210/13259
- Description: M.Com. (Economics) , Please refer to full text to view abstract
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- Authors: Van der Merwe, Ronell
- Date: 2015-02-09
- Subjects: Econometric models , Economics - South Africa
- Type: Thesis
- Identifier: uj:13234 , http://hdl.handle.net/10210/13259
- Description: M.Com. (Economics) , Please refer to full text to view abstract
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An application of the generalised autoregressive score model to market risk modelling
- Authors: Kamika, Mbuaya Grace
- Date: 2019
- Subjects: Autoregression (Statistics) , Econometric models , Risk management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/456837 , uj:40485
- Description: Abstract: This study makes use of different statistical techniques to estimate unconditional and conditional market risk measures. The unconditional measures are calculated by using three traditional Value at Risk techniques namely the Historical Simulation (HS), Variance-Covariance (VC) and Monte Carlo simulation (MCS). However, for the conditional market risk measure, this study employs a novel technique known as the Generalized Autoregressive Score (GAS) model. This technique allows us to overcome the unrealistic assumption often used in empirical studies that argue that the score of the empirical distribution when computing the conditional Value at Risk measures; is constant over time. The technique used in this study allows us to relax this assumption and let the score of the empirical distribution to evolve over time. The study begins by removing the effect of autocorrelation and heteroskedasticity in the returns series by applying an Autoregressive Moving Average Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) process. Thereafter, the filtered returns are fitted to a GAS process in order to estimate the evolving score of the empirical distribution of the returns to be used in the conditional Value at Risk computation. The study uses a sample data of daily log returns of four stock market indices: - the South African ALSI, the UK FTSE 100, the Chinese Hang Seng and the U.S. S&P 500 spanning from the 22 September 2003 to 5 November 2019. Firstly, the results of the unconditional Value at Risk measures are found to be around 3%, 5% and 2% for the HS, VC and (MCS) techniques, respectively. Secondly the estimated parameters of all the specified ARIMA-GARCH models used to filter the return series were found to be statistically significant including the leverage which suggests that bad news have a higher volatility than good news in the respective stock markets. Finally, the resulting standardized residuals were used to estimate the evolving score (parameters) of the GAS process. The estimated parameters from the GAS model show that the scores of the empirical distribution are significant and that the current score of the empirical distribution are explained by their previous score values. The market risk measures obtained with the GAS model are found to be more reliable than the ones obtained with traditional conditional Value at Risk model that assume constant score. To validate our results, the study implements three back test techniques namely, the unconditional coverage test, the conditional coverage test and the three zone test. The results support our abovementioned findings. , M.Com. (Financial Economics)
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- Authors: Kamika, Mbuaya Grace
- Date: 2019
- Subjects: Autoregression (Statistics) , Econometric models , Risk management
- Language: English
- Type: Masters (Thesis)
- Identifier: http://hdl.handle.net/10210/456837 , uj:40485
- Description: Abstract: This study makes use of different statistical techniques to estimate unconditional and conditional market risk measures. The unconditional measures are calculated by using three traditional Value at Risk techniques namely the Historical Simulation (HS), Variance-Covariance (VC) and Monte Carlo simulation (MCS). However, for the conditional market risk measure, this study employs a novel technique known as the Generalized Autoregressive Score (GAS) model. This technique allows us to overcome the unrealistic assumption often used in empirical studies that argue that the score of the empirical distribution when computing the conditional Value at Risk measures; is constant over time. The technique used in this study allows us to relax this assumption and let the score of the empirical distribution to evolve over time. The study begins by removing the effect of autocorrelation and heteroskedasticity in the returns series by applying an Autoregressive Moving Average Generalized Autoregressive Conditional Heteroscedasticity (ARMA-GARCH) process. Thereafter, the filtered returns are fitted to a GAS process in order to estimate the evolving score of the empirical distribution of the returns to be used in the conditional Value at Risk computation. The study uses a sample data of daily log returns of four stock market indices: - the South African ALSI, the UK FTSE 100, the Chinese Hang Seng and the U.S. S&P 500 spanning from the 22 September 2003 to 5 November 2019. Firstly, the results of the unconditional Value at Risk measures are found to be around 3%, 5% and 2% for the HS, VC and (MCS) techniques, respectively. Secondly the estimated parameters of all the specified ARIMA-GARCH models used to filter the return series were found to be statistically significant including the leverage which suggests that bad news have a higher volatility than good news in the respective stock markets. Finally, the resulting standardized residuals were used to estimate the evolving score (parameters) of the GAS process. The estimated parameters from the GAS model show that the scores of the empirical distribution are significant and that the current score of the empirical distribution are explained by their previous score values. The market risk measures obtained with the GAS model are found to be more reliable than the ones obtained with traditional conditional Value at Risk model that assume constant score. To validate our results, the study implements three back test techniques namely, the unconditional coverage test, the conditional coverage test and the three zone test. The results support our abovementioned findings. , M.Com. (Financial Economics)
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