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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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Detekce lineární části Patlak-Rutlandova grafu
Šmídl, Václav
Detection of linear part of a graph is a common problem in data analysis. Specifically for the Patlak-Rutland plot, this step is an important part of functional analysis of renal activity. An automated method for detection is proposed and tested on 16 data sets of real medical data.
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Dynamic decision making based on iterations-spread-in-time strategy
Šindelář, Jan ; Křivánek, O.
This article describes a formal approach to decision making optimization in commodity futures markets. Our aim was to design optimal decision strategy generating decision at a given time. It contains theoretical description of estimation using Bayesian learning and approximate methods of dynamic programming. Finally, the original decision strategy using approximate methods of dynamic programming was designed. This strategy was tested by a series of experiments indicating our ability to construct pro table trading machine.
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Transformace finančních dat určených pro dynamické rozhodování
Chudoba, M. ; Jirsa, Ladislav
In the presented work we are introduced to the problem of optimal decision making while dealing on the exchange with so-called "financial futures", i.e. time financial transaction. This task is transferred into the simplified mathematical model, which is solvable using Bayesian estimation methods. Financial data are modelled by auto-regressive model with normal noise, because the tools, which are exploited for prediction of the price on the market and which assume normal noise, have been already developed. The main goal of this work is the comparison of the efficiency of various transformations on input data, so that their noise had normal distribution, therefore the price prediction was as accurate as possible. The applicable algorithm is programmed in Matlab; the presentation of achieved results forms the final part of this thesis.
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