The application of statistical classification to predict sovereign default
- Authors: Vele, Rendani
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424563 , vital:72164
- Description: When considering sovereign loans, it is imperative for a financial institution to have a good understanding of the sovereign they are transacting with. Defaults can occur if proper evaluation steps are not considered. To aid in the prediction of potential sovereign defaults, financial institutions, together with grading companies, quantify the risk associated with issuing a loan to a sovereign by developing sovereign default early warning systems (EWS). Various classification models are considered in this study to develop sovereign default EWS. These models are the binary logit, probit, Bayesian additive regression trees, and artificial neural networks. This study investigates the predictive performance of the various classification techniques. Sovereign information is not readily available, so missing data techniques are considered in order to counter the data availability issue. Sovereign defaults are rare, which results in an imbalance in the distribution of the binary dependent variable. To assess data sets with such characteristics, metrics for imbalanced data are considered for model performance comparison. From the findings, the Bayesian additive regression technique generated better results than the other techniques when considering a basic data analysis. Moreover when cross-validation was considered, the neural network technique performed best. In addition, regional models had better results than the global model when considering model predictive capability. The significance of this study is to develop sovereign default prediction models using various classification techniques focused on enhancing previous literature and analysis through the application of Bayesian additive regression trees. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
- Full Text:
- Date Issued: 2023-10-13
- Authors: Vele, Rendani
- Date: 2023-10-13
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/424563 , vital:72164
- Description: When considering sovereign loans, it is imperative for a financial institution to have a good understanding of the sovereign they are transacting with. Defaults can occur if proper evaluation steps are not considered. To aid in the prediction of potential sovereign defaults, financial institutions, together with grading companies, quantify the risk associated with issuing a loan to a sovereign by developing sovereign default early warning systems (EWS). Various classification models are considered in this study to develop sovereign default EWS. These models are the binary logit, probit, Bayesian additive regression trees, and artificial neural networks. This study investigates the predictive performance of the various classification techniques. Sovereign information is not readily available, so missing data techniques are considered in order to counter the data availability issue. Sovereign defaults are rare, which results in an imbalance in the distribution of the binary dependent variable. To assess data sets with such characteristics, metrics for imbalanced data are considered for model performance comparison. From the findings, the Bayesian additive regression technique generated better results than the other techniques when considering a basic data analysis. Moreover when cross-validation was considered, the neural network technique performed best. In addition, regional models had better results than the global model when considering model predictive capability. The significance of this study is to develop sovereign default prediction models using various classification techniques focused on enhancing previous literature and analysis through the application of Bayesian additive regression trees. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
- Full Text:
- Date Issued: 2023-10-13
Enhancing the use of large-scale assessment data in South Africa: Multidimensional Item Response Theory
- Authors: Lahoud, Tamlyn Ann
- Date: 2023-03-29
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/422389 , vital:71938
- Description: This research aims to enhance the use of large-scale assessment data in South Africa by evaluating assessment validity by means of multidimensional item response theory and its associated statistical techniques, which have been severely underutilised. Data from the 2014 administration of the grade 6 Mathematics annual national assessment was used in this study and all analyses were conducted using the mirt package in R. A two parameter logistic item response theory model was developed which indicated a clear alignment between the model parameters and difficulty specifications of the test. The test was found to favour learners within the central band on the ability scale. An exploratory five dimensional item response theory model was then developed to investigate the alignment with the test specifications as evidence for construct validity. Significant discrepancies between the factor structure and the specifications of the test were identified. Notably, the results suggest that some items measured an ability that was not purely mathematical, such as reading ability, which would distort the test’s representation of Mathematics ability, disadvantage learners with lower English literacy, and reduce the construct validity of the test. Further validity evidence was obtained by differential item functioning analyses which revealed that fourteen items function differently for learners from different provinces. Although possible reasons for the presence of differential item functioning among provinces were not discussed, its presence provided sufficient evidence against the validity of the test. In conclusion, multidimensional item response theory provided an effective and rigorous approach to establishing the validity of a large-scale assessment. To avoid the pitfalls of the annual national assessments, it is recommended that this multidimensional item and differential item functioning techniques are utilised for the development and evaluation of future national assessment instruments in South Africa. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
- Full Text:
- Date Issued: 2023-03-29
- Authors: Lahoud, Tamlyn Ann
- Date: 2023-03-29
- Subjects: Uncatalogued
- Language: English
- Type: Academic theses , Master's theses , text
- Identifier: http://hdl.handle.net/10962/422389 , vital:71938
- Description: This research aims to enhance the use of large-scale assessment data in South Africa by evaluating assessment validity by means of multidimensional item response theory and its associated statistical techniques, which have been severely underutilised. Data from the 2014 administration of the grade 6 Mathematics annual national assessment was used in this study and all analyses were conducted using the mirt package in R. A two parameter logistic item response theory model was developed which indicated a clear alignment between the model parameters and difficulty specifications of the test. The test was found to favour learners within the central band on the ability scale. An exploratory five dimensional item response theory model was then developed to investigate the alignment with the test specifications as evidence for construct validity. Significant discrepancies between the factor structure and the specifications of the test were identified. Notably, the results suggest that some items measured an ability that was not purely mathematical, such as reading ability, which would distort the test’s representation of Mathematics ability, disadvantage learners with lower English literacy, and reduce the construct validity of the test. Further validity evidence was obtained by differential item functioning analyses which revealed that fourteen items function differently for learners from different provinces. Although possible reasons for the presence of differential item functioning among provinces were not discussed, its presence provided sufficient evidence against the validity of the test. In conclusion, multidimensional item response theory provided an effective and rigorous approach to establishing the validity of a large-scale assessment. To avoid the pitfalls of the annual national assessments, it is recommended that this multidimensional item and differential item functioning techniques are utilised for the development and evaluation of future national assessment instruments in South Africa. , Thesis (MSc) -- Faculty of Science, Statistics, 2023
- Full Text:
- Date Issued: 2023-03-29
A modelling approach to the analysis of complex survey data
- Authors: Dlangamandla, Olwethu
- Date: 2021-10-29
- Subjects: Sampling (Statistics) , Linear models (Statistics) , Multilevel models (Statistics) , Logistic regression analysis , Complex survey data
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192955 , vital:45284
- Description: Surveys are an essential tool for collecting data and most surveys use complex sampling designs to collect the data. Complex sampling designs are used mainly to enhance representativeness in the sample by accounting for the underlying structure of the population. This often results in data that are non-independent and clustered. Ignoring complex design features such as clustering, stratification, multistage and unequal probability sampling may result in inaccurate and incorrect inference. An overview of, and difference between, design-based and model-based approaches to inference for complex survey data has been discussed. This study adopts a model-based approach. The objective of this study is to discuss and describe the modelling approach in analysing complex survey data. This is specifically done by introducing the principle inference methods under which data from complex surveys may be analysed. In particular, discussions on the theory and methods of model fitting for the analysis of complex survey data are presented. We begin by discussing unique features of complex survey data and explore appropriate methods of analysis that account for the complexity inherent in the survey data. We also explore the widely applied logistic regression modelling of binary data in a complex sample survey context. In particular, four forms of logistic regression models are fitted. These models are generalized linear models, multilevel models, mixed effects models and generalized linear mixed models. Simulated complex survey data are used to illustrate the methods and models. Various R packages are used for the analysis. The results presented and discussed in this thesis indicate that a logistic mixed model with first and second level predictors has a better fit compared to a logistic mixed model with first level predictors. In addition, a logistic multilevel model with first and second level predictors and nested random effects provides a better fit to the data compared to other logistic multilevel fitted models. Similar results were obtained from fitting a generalized logistic mixed model with first and second level predictor variables and a generalized linear mixed model with first and second level predictors and nested random effects. , Thesis (MSC) -- Faculty of Science, Statistics, 2021
- Full Text:
- Date Issued: 2021-10-29
- Authors: Dlangamandla, Olwethu
- Date: 2021-10-29
- Subjects: Sampling (Statistics) , Linear models (Statistics) , Multilevel models (Statistics) , Logistic regression analysis , Complex survey data
- Language: English
- Type: Master's theses , text
- Identifier: http://hdl.handle.net/10962/192955 , vital:45284
- Description: Surveys are an essential tool for collecting data and most surveys use complex sampling designs to collect the data. Complex sampling designs are used mainly to enhance representativeness in the sample by accounting for the underlying structure of the population. This often results in data that are non-independent and clustered. Ignoring complex design features such as clustering, stratification, multistage and unequal probability sampling may result in inaccurate and incorrect inference. An overview of, and difference between, design-based and model-based approaches to inference for complex survey data has been discussed. This study adopts a model-based approach. The objective of this study is to discuss and describe the modelling approach in analysing complex survey data. This is specifically done by introducing the principle inference methods under which data from complex surveys may be analysed. In particular, discussions on the theory and methods of model fitting for the analysis of complex survey data are presented. We begin by discussing unique features of complex survey data and explore appropriate methods of analysis that account for the complexity inherent in the survey data. We also explore the widely applied logistic regression modelling of binary data in a complex sample survey context. In particular, four forms of logistic regression models are fitted. These models are generalized linear models, multilevel models, mixed effects models and generalized linear mixed models. Simulated complex survey data are used to illustrate the methods and models. Various R packages are used for the analysis. The results presented and discussed in this thesis indicate that a logistic mixed model with first and second level predictors has a better fit compared to a logistic mixed model with first level predictors. In addition, a logistic multilevel model with first and second level predictors and nested random effects provides a better fit to the data compared to other logistic multilevel fitted models. Similar results were obtained from fitting a generalized logistic mixed model with first and second level predictor variables and a generalized linear mixed model with first and second level predictors and nested random effects. , Thesis (MSC) -- Faculty of Science, Statistics, 2021
- Full Text:
- Date Issued: 2021-10-29
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