This thesis deals with the development, implementation and application of statistical modeling techniques which can be employed in the analysis of credit ratings. Credit ratings are one of the most widely used measures of credit risk and are relevant for a wide array of financial market participants, from investors, as part of their investment decision process, to regulators and legislators as a means of measuring and limiting risk. The majority of credit ratings is produced by the "Big Three" credit rating agencies Standard & Poors', Moody's and Fitch. Especially in the light of the 2007-2009 financial crisis, these rating agencies have been strongly criticized for failing to assess risk accurately and for the lack of transparency in their rating methodology. However, they continue to maintain a powerful role as financial market participants and have a huge impact on the cost of funding. These points of criticism call for the development of modeling techniques that can 1) facilitate an understanding of the factors that drive the rating agencies' evaluations, 2) generate insights into the rating patterns that these agencies exhibit. This dissertation consists of three research articles. The first one focuses on variable selection and assessment of variable importance in accounting-based models of credit risk. The credit risk measure employed in the study is derived from credit ratings assigned by ratings agencies Standard & Poors' and Moody's. To deal with the lack of theoretical foundation specific to this type of models, state-of-the-art statistical methods are employed. Different models are compared based on a predictive criterion and model uncertainty is accounted for in a Bayesian setting. Parsimonious models are identified after applying the proposed techniques. The second paper proposes the class of multivariate ordinal regression models for the modeling of credit ratings. The model class is motivated by the fact that correlated ordinal data arises naturally in the context of credit ratings. From a methodological point of view, we extend existing model specifications in several directions by allowing, among others, for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. The estimation of the proposed models is performed using composite likelihood methods. Insights into the heterogeneity among the "Big Three" are gained when applying this model class to the multiple credit ratings dataset. A comprehensive simulation study on the performance of the estimators is provided. The third research paper deals with the implementation and application of the model class introduced in the second article. In order to make the class of multivariate ordinal regression models more accessible, the R package mvord and the complementary paper included in this dissertation have been developed. The mvord package is available on the "Comprehensive R Archive Network" (CRAN) for free download and enhances the available ready-to-use statistical software for the analysis of correlated ordinal data. In the creation of the package a strong emphasis has been put on developing a user-friendly and flexible design. The user-friendly design allows end users to estimate in an easy way sophisticated models from the implemented model class. The end users the package appeals to are practitioners and researchers who deal with correlated ordinal data in various areas of application, ranging from credit risk to medicine or psychology.

In South Africa and other parts of sub-Saharan Africa, market participation of smallholder farmers are rapidly being advocated as a means to achieve the United Nations Sustainable Development Agenda’s (SDGs): zero hunger and no poverty. Yet little is known about market participation in the sunflower industry. The study therefore, examine market participation of smallholder sunflower farmers in Ngaka Modiri Molema District Municipality, North West Province, South Africa. A quantitative research approach was developed to address the research objectives, and a proportional stratified random sampling technique was used to select 177 sunflower producing households. Respondents information were captured using semi-structured questionnaires, data were then entered and coded using statistical software computer programs (MS Excel, SPSS, and Stata). Socio-economic characteristics, level of market participation, and factors influencing households market participation within the district were analyzed using descriptive statistics, household commercialization index, and probit regression model. Overall, the result indicates that respondents exhibited high level of commercialization (90.1 % market participants). While, the result of the probit regression model shows that eight (8) of the regressors were statistically significant. Variables such as age (Coef = 0.103, p<0.01), gender (Coef = 1.267, p<0.05), market outlet (Coef = 1.351, p<0.01), access to information (Coef = 1.298, p<0.05), and quantity sold in tons (Coef = 0.015, p<0.010) were found to have positive and statistically significant influence, while household size (Coef = -0.409, p<0.01), market distance (Coef = 0.618, p<0.010) and land tenure system (Coef = -1.541, p<0.05) were found to have a negative but statistically significant impact on market participation among respondents. The pseudo R2 of the probit model is 0.5199, indicating that the model matches the dataset and the regressors accurately explains 51.99 % of the variation. The overall goodness of fit measures of the probit model was determined using postestimation test for predictive margin. With a high significant chi-square value of (p<0.0001), the result correctly predicted a 90 % likelihood of respondents to participate in the market. The findings suggests that rural-based initiatives and intervention programs be developed to boost households' access to finance, grants, and diversified markets for effective market competitiveness, while there is a greater need for proper awareness, supports, and partnerships focused on promoting women and youth participation in the sunflower sector across the district. / Agriculture and  Animal Health / M. Sci. (Agriculture)