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Issues in adopting DSGE models for use in the policy process
Fukač, Martin ; Pagan, Adrian
This discussion is structured by three concerns – model design, matching the data and operational requirements. The paper begins with a general discussion of the structure of dynamic stochastic general equilibrium (DSGE) models where writers investigate issues like (i) the type of restrictions being imposed by DSGE models upon system dynamics, (ii) the implication these models would have for "location parameters", viz. growth rates, and (iii) whether these models can track the long-run movements in variables as well as matching dynamic adjustment.
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Statistical inference for categorical data analysis
Kocáb, Jan ; Pecáková, Iva (advisor) ; Coufalová, Petra (referee)
This thesis introduces statistical methods for categorical data. These methods are especially used in social sciences such as sociology, psychology and political science, but their importance has increased also in medical and technical sciences. In the first part there is mentioned statistical inference for a proportion. Here is written about classical, exact and Bayesian methods for estimating and hypothesis testing. If we have a large sample then we can approximate exact distribution by normal distribution but if we have a small sample cannot use this approximation and it is necessary to use discrete distribution which makes inference more complicated. The second part deals with two categorical variables analysis in contingency tables. Here are explained measures of association for 2 x 2 contingency tables such as difference of proportion and odds ratio and also presented how we can test independence in the case of large sample and small one. If we have small sample we are not allowed to use classical chi-squared tests and it is necessary to use alternative methods. This part contains variety of exact tests of independence and Bayesian approach for the 2 x 2 table too. In the end of this part there is written about a table for two dependent samples and we are interested whether two variables give identical results which occurs when marginal proportions are equal. In the last part there are methods used on data and discussed results.
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History of bayesian statistics
Karel, Tomáš ; Hebák, Petr (advisor) ; Vilikus, Ondřej (referee)
The historical development of statistical theories contains a particular combat between Clasical (frequentist) and Bayessian School. This thesis brings near the history of this development, elaborates on possibilities about future applicability of Bayessian approach into other scientific areas and usability in teaching basic courses of statistics. The thesis summarizes important contributors and their significant contributions in Bayessian field. Their work helped to spread these methodes within scientific community. We briefly describe the differences between frequentist and bayesian approach. These are further demonstrated on two motivation examples. The same is carried on demostration of the differences between subjective approach and objective approach in probabilistic understanding and its influence on conducting the result. This work is aimed as the short introductory text for future students with the same for Bayessians as I posses.
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První experimenty s distribuovaným Bayesovským rozhodováním
Šmídl, Václav ; Andrýsek, Josef
Decision-making under uncertainty is a natural part of everyday life of every human being. In societal science, various aspects of decision-making were studied, mostly in the area of psychology. In technical science, the process was formalized using probability theory yielding so called Bayesian theory of decision making. However, one of the key assumptions of this theory is that the decision-maker is the only entity that intentionally influences the system. This assumption is certainly violated in more complicated systems, such as human society or distributed control. Recently, a series of papers attempts to offer an extension of the Bayesian theory for many decision-makers, i.e. decentralized stochastic control. Since there are no proofs of optimality of the proposed Bayesian distributed decision making available in the literature, we study this approach via experimental simulation studies.
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