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Application of Multilevel Models in Geography: Example of Unemployment
Kadlecová, Adéla ; Netrdová, Pavlína (advisor) ; Komárek, Marek (referee)
Application of Multilevel Modeling in Geography: Example of Unemployement Abstract Multilevel models are an effective methodological tool for analyzing hierarchically structured data. It is a quantitative method based on regression analysis whose use in geography is currently not so widespread. In view of this fact this work is aimed especially at a detailed introduction of the statistical method of multilevel modeling, including both an introduction to all its possibilities and limitations, as well as an example of application of this method in the specific geographic research from the area of unemployment. The purpose of this analysis is to identify particular individual and regional variables related to unemployment and to uncover the effect of these variables on unemployment. Keywords: multilevel modeling, quantitative methods, unemployment, Czechia
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Analysis of the impact of Retail bank’s price-setting policy change
Lehocký, Dominik ; Zouhar, Jan (advisor) ; Dlouhá, Zuzana (referee)
The environment of low interest rates results in a sharp competition in the banking industry. To stay competitive, banks must be able to respond to rival offers practically immediately. For this purpose, so-called price exceptions are being used. Their use, however, results in a large amount of administrative burden. To alleviate this administrative burden, the bankers were given some competencies regarding pricing. There is a concern, however that this change may be accepted as a flat-rate price cut. It is for this reason that it is necessary to analyse on real data how this change in the internal policy of Retail Bank is manifested. The problem, however, is that there are several factors that cannot always be recorded in the process of data collection. This is precisely why this analysis estimates the impact of new competences using multilevel models. These allow for a certain form of variability for specific groups of observations and consider the presence of unattended factors.
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