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Proměnlivost multiplikátorů vládních výdajů v čase: Evidence z dat z USA
Focht, Daniel ; Maršál, Aleš (advisor) ; Chytilová, Helena (referee)
This paper estimates the size of the government spending multiplier over different states of the economy. Previous research came with two contradictory conclusions. Part of the literature argues that the spending multiplier is larger during recession and zero-lower bound periods, while the second one concludes that it remains constant. First, a summary of the relevant literature is presented, outlining different types of used methodological approaches and estimated size of the multiplier. We build a model estimated using local projections by Jorda for the period 1889 to 2016 to estimate government spending multipliers over different states of the economy. Our results show that the spending multiplier remains constant over different states of the economy.
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Estimate VAT selection after the introduction of electronic evidence of sales in the Czech Republic from 2016
Píchal, Dominik ; Pikhart, Zdeněk (advisor) ; Zeman, Martin (referee)
The Bachelors Thesis focuses on the topic of the tax collection and other subjects that are related to the topic like underground economy, tax evasion and other instruments that lead to the efficiency improvement of the tax collection. Electronic evidence of taxes in Czech is the kind of the instrument that aims towards the increase of the tax collection and improvement of the control of the taxpayer. Comparation and analysis was used for its methodological basis. Analysis and comparison serves as the proof that the thesis, of the electronic evidence of taxes beeing an effective instrument, is correct. The merit of the thesis is an overall description of the chosen phenomena affecting the tax collection, description of the models of electronic evidence of taxes from abroad and most importantly description and analysis of the upcoming czech one.
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Empirical analysis of Okun’s law in Iceland
Zajíček, Zdeněk ; Slaný, Martin (advisor) ; Chytilová, Helena (referee)
This thesis deals with empirical analysis of Okuns law in Iceland. Okuns hypothesis of negative relationship between real GDP and the rate of unemployment is being tested on two models, difference and gap, using OLS estimation. Also there are two filtration methods used (Hodrick-Prescott and Baxter-King) for gap model estimation. The results of all models showed weak relationship of variables, but proved the hypothesis. In the following part, the same procedure is being used on Finlands data, to get comparison of coefficients. Results for Finland showed weaker bond of variables than in Iceland, but the Okuns hypothesis still holds. Last part is focused on finding the sensitivity of rate of unemployment to changes in added value of each economical sector in Iceland using the production approach model. This model gave inconclusive results due to insufficient data available.
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The analysis of price elasticity of demand for beer
Hromadníková, Kateřina ; Mirvald, Michal (advisor) ; Babin, Jan (referee)
The thesis analyses price elasticity of beer demand. Hypothesis about inelastic demand is tested first for nationwide level and then on data of specific brewery. Elasticity was determined by regression analysis, specifically by ordinary least squares with all variables expressed in logarithmic form. Consumption of beer is the endogenous variable and price of bottled beer (price of one hectoliter of beer in case of brewery), average gross income and price level are in the role of the exogenous variables. The hypothesis about inelastic demand was successfully proved. Price elasticity estimates range from -0,66 to -0,2. In case of specific brewery price was not significant. On the other hand, average gross income seems to be the significant determinant. According to income elasticity beer seems to be luxury good in case of specific brewery and necessity in case of nationwide level.
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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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Zjednodušení kvantových obvodů pro modulární umocňování
Fišer, Petr ; Ivánek, Jiří (advisor) ; Nentvich, Libor (referee)
This thesis is based on top of the previous thesis "Security of modern encryption protocols" where we introduced a new paradigm for constructing quantum circuits. We have built circuits for modular arithmetic (addition, multiplication and exponentiation) in order to break El-Gamal asymmetric cryptosystem. Current thesis reviews all proposed circuits and discusses possibilities of their further optimization in goal of lowering the number of used qbits at least by an order of magnitude. It also shows that this is not possible due to existence of COPY gates which make the design inherently unoptimizable. Getting rid of COPY gates is, however, not possible without substantial rewrite of the whole paradigm. The overall estimate of number of qbits used in circuits thus remains O(log(m)log^2(N)) where m is a processed number and N is a modulus. The thesis also proposes optimization of the modular multiplication circuit that, if issues with COPY gates are resolved, allows us to lower the number of used qbits by about O(log(m)) at the price of a longer execution time.
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Diagnostics for Robust Regression: Linear Versus Nonlinear Model
Kalina, Jan
Robust statistical methods represent important tools for estimating parameters in linear as well as nonlinear econometric models. In contrary to the least squares, they do not suffer from vulnerability to the presence of outlying measurements in the data. Nevertheless, they need to be accompanied by diagnostic tools for verifying their assumptions. In this paper, we propose the asymptotic Goldfeld-Quandt test for the regression median. It allows to formulate a natural procedure for models with heteroscedastic disturbances, which is again based on the regression median. Further, we pay attention to nonlinear regression model. We focus on the nonlinear least weighted squares estimator, which is one of recently proposed robust estimators of parameters in a nonlinear regression. We study residuals of the estimator and use a numerical simulation to reveal that they can be severely heteroscedastic also for data generated from a model with homoscedastic disturbances. Thus, we give a warning that standard residuals of the robust nonlinear estimator may produce misleading results if used for the standard diagnostic tools
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On Exact Heteroscedasticity Testing for Robust Regression
Kalina, Jan ; Peštová, Barbora
The paper is devoted to the least weighted squares estimator, which is one of highly robust estimators for the linear regression model. Novel permutation tests of heteroscedasticity are proposed. Also the asymptotic behavior of the permutation test statistics of the Goldfeld-Quandt and Breusch-Pagan tests is investigated. A numerical experiment on real economic data is presented, which also shows how to perform a robust prediction model under heteroscedasticity.
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