Selection of Econometric Instruments when Building a Scoring Model Based on Dummy Variables

The aim of the study is to select adequate econometric instruments for building a scoring model on a specific array of initial data, which contains the vast majority of fictitious variables. Despite a significant number of developments devoted to the construction of scoring models, a universal method allowing to obtain a highly efficient classifier for any data has not been identified. Therefore, the task of selection of the best method for building a scoring model remains relevant, depending on the characteristics of the available data. The most successful approach when selecting a model for solving the problem of binary classification is the use of several types of econometric models and the choice of the best of them according to the results of classification. In the presented study, the following types of models were applied: discriminant model, logit and probit regressions, and polynomial logistic regression. Training samples with different structure were used. Comparison of all obtained models allows us to conclude that polynomial logistic regression is preferable in this case. This model demonstrates high classification rates for all introduced object classes and has an important advantage compared to models that make a binary selection. The advantage of polynomial logistic regression is also the possibility of selecting in each case a convenient scale for dividing borrowers into more than two classes and determining the level of probability of reliability of the borrower acceptable for its own conditions, at which it should be assigned to one of the selected classes. Prospects for further research in this direction are the use of machine learning methods that will be able to use ensembles of the best of the considered models. In addition, the proposed models can be used in solving similar problems in other spheres of economic activity