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Clustering and regression analysis of micro panel data
Sobíšek, Lukáš ; Pecáková, Iva (advisor) ; Komárek, Arnošt (referee) ; Brabec, Marek (referee)
The main purpose of panel studies is to analyze changes in values of studied variables over time. In micro panel research, a large number of elements are periodically observed within the relatively short time period of just a few years. Moreover, the number of repeated measurements is small. This dissertation deals with contemporary approaches to the regression and the clustering analysis of micro panel data. One of the approaches to the micro panel analysis is to use multivariate statistical models originally designed for crosssectional data and modify them in order to take into account the within-subject correlation. The thesis summarizes available tools for the regression analysis of micro panel data. The known and currently used linear mixed effects models for a normally distributed dependent variable are recapitulated. Besides that, new approaches for analysis of a response variable with other than normal distribution are presented. These approaches include the generalized marginal linear model, the generalized linear mixed effects model and the Bayesian modelling approach. In addition to describing the aforementioned models, the paper also includes a brief overview of their implementation in the R software. The difficulty with the regression models adjusted for micro panel data is the ambiguity of their parameters estimation. This thesis proposes a way to improve the estimations through the cluster analysis. For this reason, the thesis also contains a description of methods of the cluster analysis of micro panel data. Because supply of the methods is limited, the main goal of this paper is to devise its own two-step approach for clustering micro panel data. In the first step, the panel data are transformed into a static form using a set of proposed characteristics of dynamics. These characteristics represent different features of time course of the observed variables. In the second step, the elements are clustered by conventional spatial clustering techniques (agglomerative clustering and the C-means partitioning). The clustering is based on a dissimilarity matrix of the values of clustering variables calculated in the first step. Another goal of this paper is to find out whether the suggested procedure leads to an improvement in quality of the regression models for this type of data. By means of a simulation study, the procedure drafted herein is compared to the procedure applied in the kml package of the R software, as well as to the clustering characteristics proposed by Urso (2004). The simulation study demonstrated better results of the proposed combination of clustering variables as compared to the other combinations currently used. A corresponding script written in the R-language represents another benefit of this paper. It is available on the attached CD and it can be used for analyses of readers own micro panel data.
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Use of Interest Rate Models for Interest Rate Risk Management in the Czech Financial Market Environment
Cíchová Králová, Dana ; Arlt, Josef (advisor) ; Cipra, Tomáš (referee) ; Witzany, Jiří (referee)
The main goal of this thesis is to suggest an appropriate approach to interest rate risk modeling in the Czech financial market environment in various situations. Three distinct periods are analyzed. These periods, which are the period before the global financial crisis, period during the financial crisis and in the aftermath of the global financial crisis and calming subsequent debt crisis in the eurozone, are characterized by different evaluation of liquidity and credit risk, different relationship between financial variables and market participants and different degree of market regulations. Within this goal, an application of the BGM model in the Czech financial market environment is crucial. Use of the BGM model for the purpose of predicting a dynamics of a yield curve is not very common. This is firstly due to the fact that primary use of this model is a valuation of interest rate derivatives while ensuring the absence of arbitrage and secondly its application is relatively difficult. Nevertheless, I apply the BGM model to obtain predictions of the probability distributions of interest rates in the Czech and eurozone market environment, because its complexity, direct modeling of a yield curve based on market rates and especially a possibility of parameter estimation based on current swaptions volatilities quotations may lead to a significant improvement of predictions. This improvement was also confirmed in this thesis. Use of swaptions volatilities market quotations is especially useful in the period of unprecedented mone- tary easing and increased number of central banks and other regulators interventions into financial markets that occur after the financial crisis, because it reflects current market expectations which also include future interventions. As a consequence of underdevelopment of the Czech financial market there are no market quotations of Czech koruna denominated swaptions volatilities. I suggest their approximations based on quotations of euro denominated swaptions volatilities and also using volatilities of koruna and euro forward rates. Use of this approach ensures that predictions of the Czech yield curve dynamics contain current market expectations. To my knowledge, any other author has not presented similar application of the BGM model in the Czech financial market environment. In this thesis I further predict a Czech and Euro area money market yield curve dynamics using the CIR and the GP models as representatives of various types of interest rates models to compare these predictions with BGM predictions. I suggest a comprehensive system of three criteria, based on comparison of predicti- ons with reality, to describe a predictive power of selected models and an appropria- teness of their use in the Czech market environment during different situations in the market. This analysis shows that predictions of the Czech money market yield curve dynamics based on the BGM model demonstrate high predictive power and the best 8 quality in comparison with other models. GP model also produces relatively good qua- lity predictions. Conversely, predictions based on the CIR model as a representative of short rate model family completely failed when describing reality. In a situation when the economy allows negative rates and there is simultaneously a significant likelihood of their implementation, I recommend to obtain predictions of Czech money market yield curve dynamics using GP model which allows existence of negative interest rates. This analysis also contains a statistical test for validating the predictive power of each model and information on other tests. Berkowitz test rejects a hypothesis of accurate predictions for each model. However, this fact is common in real data testing even when using relatively good model. This fact is especially caused by difficult fulfilment of test conditions in real world. To my knowledge, such an analysis of the predictive power of selected interest rate models moreover in the Czech financial market environment has not been published yet. The last goal of this thesis is to suggest an appropriate approach to obtaining pre- dictions of Czech government bonds risk premium dynamics. I define this risk premium as a difference between government bond yields and fixed rate of CZK IRS with the same length. I apply the GP model to describe the dynamics of this indicator of the Czech Republic credit risk. In order to obtain a time series of the risk premium which are necessary for estimation of GP model parameters I firstly estimate yield curves of Czech government bonds using Svensson model for each trading day since 2005. Resulting si- mulations of risk premium show that the GP model predicts the real development of risk premiums of all maturities relatively well. Hence, the proposed approach is suitable for modeling of Czech Republic credit risk based on the use of information extracted from financial markets. I have not registered proposed approach to risk premium modeling moreover in the Czech financial market environment in other publications.
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Habitat colonization by neophyte Impatiens glandulifera and estimate of factors limiting its spread
Marková, Zuzana ; Hejda, Martin (advisor) ; Malíková, Lenka (referee)
Invasive spread of neophyte Impatiens glandulifera in central Europe started approximatelly eighty years ago. First records of dense cover stands come from belt stands in riparian habitats. The scale of invaded habitats and degree of the dominance of I. glandulifera is more diversified nowadays. This thesis is objected on the dominance and fertility of I. glandulifera within different habitat types and scale of invaded habitats in different parts of invaded range within Europe (i. e. in Czech Republic and Switzerland). The results show that the height and cover (substitutes for biomass and dominance) of this neophyte (i) correlates with the character of invaded vegetation (ii) relates to the degree of hemeroby (a measure of human impact) negativelly, and (iii), of course, both the growth and dominance are positively affected by nutrient content. Fertility does not differ among the types of invaded habitats, but goes up with the height of I. glandulifera and decreases with its cover. Invaded habitats comprises ruderal and riparian vegetation, but also wet maedows, forest clearances, beach and slope forests or weed vegetation.
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Dynamic model of human resources in project management
Hančar, Michal ; Mildeová, Stanislava (advisor) ; Šviráková, Eva (referee)
This thesis is focused on dynamics of soft factors influencing workers during projects. These factors include motivation, workplace atmosphere, team synergy of workers and their emotions, and attribute of project manager who manages the project. Identification of soft factors and their relationships was achieved by examination of scientific literature in psychology and system dynamics. Description of managing project matters was achieved by examination of scientific literature dealing with project management. The main objective of this thesis is to create a dynamic model which simulates behavior of these soft factors influencing the project staff. The primary metric of the model is efficiency of workers participating on the project based on input parameters. Validation of the model was achieved by verification of historic behavior of key elements. Results of validation experiments correspond with historic behavior with roughly 95 % accuracy. At the end of this thesis is presented an ICT project case study. Based on the results of simulation experiments is performed a scenario analysis which tries to bring possible suggestions for project management.
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Modelling, parameter estimation, optimisation and control of transport and reaction processes in bioreactors.
ŠTUMBAUER, Václav
With the significant potential of microalgae as a major biofuel source of the future, a considerable scientific attention is attracted towards the field of biotechnology and bioprocess engineering. Nevertheless the current photobioreactor (PBR) design methods are still too empirical. With this work I would like to promote the idea of designing a production system, such as a PBR, completely \emph{in silico}, thus allowing for the in silico optimization and optimal control determination. The thesis deals with the PBR modeling and simulation. It addresses two crucial issues in the current state-of-the-art PBR modeling. The first issue relevant to the deficiency of the currently available models - the incorrect or insufficient treatment of either the transport process modeling, the reaction modeling or the coupling between these two models. A correct treatment of both the transport and the reaction phenomena is proposed in the thesis - in the form of a unified modeling framework consisting of three interconnected parts - (i) the state system, (ii) the fluid-dynamic model and (iii) optimal control determination. The proposed model structure allows prediction of the PBR performance with respect to the modelled PBR size, geometry, operating conditions or a particular microalgae strain. The proposed unified modeling approach is applied to the case of the Couette-Taylor photobioreactor (CTBR) where it is used for the optimal control solution. The PBR represents a complex multiscale problem and especially in the case of the production scale systems, the associated computational costs are paramount. This is the second crucial issue addressed in the thesis. With respect to the computational complexity, the fluid dynamics simulation is the most costly part of the PBR simulation. To model the fluid flow with the classical CFD (Computational Fluid Dynamics) methods inside a production scale PBR leads to an enormous grid size. This usually requires a parallel implementation of the solver but in the parallelization of the classical methods lies another relevant issue - that of the amount of data the individual nodes must interchange with each other. The thesis addresses the performance relevant issues by proposing and evaluation alternative approaches to the fluid flow simulation. These approaches are more suitable to the parallel implementation than the classical methods because of their rather local character in comparison to the classical methods - namely the Lattice Boltzmann Method (LBM) for fluid flow, which is the primary focus of the thesis in this regard and alternatively also the discrete random walk based method (DRW). As the outcome of the thesis I have developed and validated a new Lagrangian general modeling approach to the transport and reaction processes in PBR - a framework based on the Lattice Boltzmann method (LBM) and the model of the Photosynthetic Factory (PSF) that models correctly the transport and reaction processes and their coupling. Further I have implemented a software prototype based on the proposed modeling approach and validated this prototype on the case of the Coutte-Taylor PBR. I have also demonstrated that the modeling approach has a significant potential from the computational costs point of view by implementing and validating the software prototype on the parallel architecture of CUDA (Compute Unified Device Architecture). The current parallel implementation is approximately 20 times faster than the unparallized one and decreases thus significantly the iteration cycle of the PBR design process.
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NUMERICAL ESTIMATION OF MICRO-CRACK PATHS IN POLYMER PARTICULATE COMPOSITE
Majer, Z. ; Náhlík, Luboš
Determination of composite mechanical behavior is one of important part during the composite tailoring. The aim of the present work was to estimate a micro-crack behavior in a polymer particulate composite. The composite was investigated by means of the finite element method -using ANSYS software. A two-dimensional three-phase finite element model was developed to analyze the crack growth behavior. The assumptions of the linear elastic fracture mechanics were considered and the Maximum Tangential Stress (MTS) criterion was used to predict the direction of the crack propagation. The effect of the elastic modulus of the interphase on the micro-crack propagation was investigated. The properties of matrix and particles were taken from experiment. It was shown that the interphase properties influence the stress intensity factor KI as well as the micro-crack paths. The results of this paper can contribute to a better understanding of the micro-crack propagation in particulate composites with respect to the interphase.
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Intraday Dynamics of Euro Area Sovereign Credit Risk Contagion
Komárek, Luboš ; Ters, Kristyna ; Urban, Jörg
We examine the role of the CDS and bond markets during and before the recent euro area sovereign debt crisis as transmission channels for credit risk contagion between sovereign entities. We analyse an intraday dataset for GIIPS countries as well as Germany, France and central European countries. Our findings suggest that, prior to the crisis, the CDS and bond markets were similarly important in the transmission of financial shock contagion, but that the importance of the bond market waned during the crisis. We find flight-to-safety effects during the crisis in the German bond market that are not present in the pre-crisis sample. Our estimated sovereign risk contagion was greater during the crisis, with an average timeline of one to two hours in GIIPS countries. By using an exogenous macroeconomic news shock, we can show that, during the crisis period, increased credit risk was not related to economic fundamentals. Further, we find that central European countries were not affected by sovereign credit risk contagion, independent of their debt level and currency.
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Do Consumers Really Follow a Rule of Thumb? Three Thousand Estimates from 130 Studies Say “Probably Not”
Havránek, Tomáš ; Sokolova, Anna
We show that three factors combine to explain the mean excess sensitivity reported in studies estimating consumption Euler equations: the use of macro data, publication bias, and liquidity constraints. When micro data are used, publication bias is corrected for, and the households under examination do not face liquidity constraints, the literature implies no evidence for the excess sensitivity of consumption to income. Hence little remains for pure rule-of-thumb behavior. The results hold when we control for 45 additional variables reflecting the methods employed by researchers and use Bayesian model averaging to account for model uncertainty. The estimates of excess sensitivity are also systematically affected by the order of approximation of the Euler equation, the treatment of non-separability between consumption and leisure, and the choice of proxy for consumption.
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Construction and scientific implementation of mathematical models for tree compartments of broadleaved trees in growth stages of seedlings and young stand
Pajtík, Jozef ; Konopka, Bohdan (advisor) ; Monika, Monika (referee)
Importance of precise estimation for tree biomass in forests has been continuously increasing. Regarding to the climate change, scientists have started to quantify all tree components not only in terms of energetic utilization but also for carbon stock estimation. Increasing relevance of biomass models for young trees relates to expanding area of young forest stands during the last period due to decay of old forests often caused by disturbances (especially: windstorms, outbreaks of bark beetles, drought episodes, and forest fires). Models for biomass stock estimations constructed for stands with age to 10 years are rare and usually are focused on aboveground tree parts. Thus, this work aims at filling knowledge gaps in this field. Its main objectives are:
1) construction of regression models applicable for estimation of dry mass in the particular tree components (i.e. stem, branches, foliage, roots) for young stands of some broadleaved species,
2) implementation of regression models for calculation of biomass conversion and expansion factors (BCEF), allocation coefficient, growth efficiency and leaf area index (LAI) and their inter-specific comparison,
3) utilisation of allometric relations for estimation on forage potential for ruminating ungulate game (i.e. browsing and stripping).
To make up the models, destructive tree sampling will be implemented. The sample trees will be excavated, separated into tree components, dried for constant weight and weighed. Log-transformed relationships will be used for construction of regression models.
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