
Bayesovský odhad DSGE modelů
Bouda, Milan ; Pánková, Václava (advisor) ; Kodera, Jan (referee) ; Lukáš, Ladislav (referee)
Thesis is dedicated to Bayesian Estimation of DSGE Models. Firstly, the history of DSGE modeling is outlined as well as development of this macroeconometric field in the Czech Republic and in the rest of the world. Secondly, the comprehensive DSGE framework is described in detail. It means that everyone is able to specify or estimate arbitrary DSGE model according to this framework. Thesis contains two empirical studies. The first study describes derivation of the New Keynesian DSGE Model and its estimation using Bayesian techniques. This model is estimated with three different Taylor rules and the best performing Taylor rule is identified using the technique called Bayesian comparison. The second study deals with development of the Small Open Economy Model with housing sector. This model is based on previous study which specifies this model as a closed economy model. I extended this model by open economy features and government sector. Czech Republic is generally considered as a small open economy and these extensions make this model more applicable to this economy. Model contains two types of households. The first type of consumers is able to access the capital markets and they can smooth consumption across time by buying or selling financial assets. These households follow the permanent income hypothesis (PIH). The other type of household uses rule of thumb (ROT) consumption, spending all their income to consumption. Other agents in this economy are specified in standard way. Outcomes of this study are mainly focused on behavior of house prices. More precisely, it means that all main outputs as Bayesian impulse response functions, Bayesian prediction and shock decomposition are focused mainly on this variable. At the end of this study one macroprudential experiment is performed. This experiment comes up with answer on the following question: is the higher/lower Loan to Value (LTV) ratio better for the Czech Republic? This experiment is very conclusive and shows that level of LTV does not affect GDP. On the other hand, house prices are very sensitive to this LTV ratio. The recommendation for the Czech National Bank could be summarized as follows. In order to keep house prices less volatile implement rather lower LTV ratio than higher.


Predictive Modeling in Credit Risk Management
Švastalová, Iva ; Dlouhá, Zuzana (advisor) ; Bouda, Milan (referee)
The diploma thesis is focused on predictive modeling in credit risk management. Banks and financial institutions are mainly interested in it to estimate the probability of client's default in order to make a decision about which client will be accepted and which client will be rejected. The theoretical part includes an introduction of credit scoring and a description of discrete choice models. The linear probability model, the probit model and the logit model are described in detail. The logit model is afterwards used for the prediction of client's default. The practical part is focused on a statistical description of the dataset and a description of how to work with it before we start with the development of the credit scoring model. After that follows the estimation of the model on testing sample, its testing and the estimation of the model on full sample with a description of individual steps of calculation and outputs of the program SPSS.


MundellFleming model. Application to the Czech economy.
Bouda, Milan ; Pánková, Václava (advisor) ; Křepelová, Marika (referee)
Interpretation of MundellFleming (MF) model is very similar to IS  LM model. The main difference is that MF model is based on an assumption of small open economy. This openness is making this model more realistic then IS  LM model. These assumptions are suitable for Czech economy. In this thesis, model is estimated and interpreted. The most important is an application to Czech economy concerning the period 2002  2010. There are ex post and ex ante predictions based on the estimated reduced form of the model. The ex post forecast is used for the purpose of evaluating whether the model is suitable for the prediction. After finding relevant suitability, prediction of endogenous variables is performed in the following four seasons.
