Institute of Mathematics and Statistical Modeling
Economical modelling has a long standing history within the University, mainly centered around the Institution (and it’s legal predecessors). The task of economical modelling is diverse, the accomplishments of the Institution’s staff are regarded as a benchmark reference in many areas.
We cultivate the following areas, among others:
- mathematics; analysis essential for producing economic and social analyses; probability calculation; studying and application of algebra;
- descriptive and inferential statistics; and econometrics, which is one of the cornerstones of virtually any quantitative analysis for economic or business purposes;
- actuarial sciences, including not only insurance mathematics in its narrow sense, but also demography, financial models of insurance contracts, and the use of statistics for insurance purposes;
- game theory, in the development of which both mathematics and economics play a decisive role;
- operations research, which aims to use resources efficiently;
- data analysis, which means, among others, the use of information available within a company for business purposes;
- narrower areas include stochastics of financial processes; quantitative models of energetics; models of national accounts; price models; welfare research; spatial analyses (Spatial Statistics).
Numerous studies in the field of economic modelling have been published in the most prestigious international journals. Educational activities are of two layers, on the one hand, we teach a wide range of methodological subjects that are essential for studying economics; and, on the other hand, we satisfy the needs, in a methodologically sound manner, of students who are interested in serious and deep mathematics, statistics, operations research, and data analysis.
Events, success stories, and what we are proud of
Research Seminars at the Institute
Topic: MSMI research seminar: Szabina Fodor, Réka Vas, László Kovács
Time: Feb 11, 2022 01:30 PM
BudapestJoin Zoom Meetinghttps://us06web.zoom.us/j/87267674654?pwd=S2QyNFR1WUthTElKUUpDeFg3eXpYZz09
Meeting ID: 872 6767 4654
Modelling students’ performance is one of the most challenging and exciting research areas of data analytics in higher education. This performance is influenced by multiple factors and is not accurately measured by the traditional five-point scale thus making this field attractive. The widespread availability of educational datasets further catalyses its popularity. In the present work, the authors investigate the performance of students starting their studies in the autumns of 2011, 2012, and 2013 at Corvinus University of Budapest, in the undergraduate business informatics training program, using variables available from the Neptun Education System. To accurately measure student performance, a new metric was developed that describes student performance on a broader set of values than the traditional five-point grade. This new metric is used to investigate the correlations between students’ performances in subjects, applying structural equation models (SEM). The results show that through SEM, substantial relationships can be revealed between the investigated courses. A proper, interdependent system of prerequisites in the curriculum may be developed based on these results. The methodology presented in this study can also be used to anticipate possible difficulties students may face in terms of certain subjects and to offer tailored help.