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Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Kalina, Jan ; Vidnerová, P.
Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles.
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Application Of Implicitly Weighted Regression Quantiles: Analysis Of The 2018 Czech Presidential Election
Kalina, Jan ; Vidnerová, Petra
Regression quantiles can be characterized as popular tools for a complex modeling of a continuous response variable conditioning on one or more given independent variables. Because they are however vulnerable to leverage points in the regression model, an alternative approach denoted as implicitly weighted regression quantiles have been proposed. The aim of current work is to apply them to the results of the second round of the 2018 presidential election in the Czech Republic. The election results are modeled as a response of 4 demographic or economic predictors over the 77 Czech counties. The analysis represents the first application of the implicitly weighted regression quantiles to data with more than one regressor. The results reveal the implicitly weighted regression quantiles to be indeed more robust with respect to leverage points compared to standard regression quantiles. If however the model does not contain leverage points, both versions of the regression quantiles yield very similar results. Thus, the election dataset serves here as an illustration of the usefulness of the implicitly weighted regression quantiles.
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The 2020 Election In The United States: Beta Regression Versus Regression Quantiles
Kalina, Jan
The results of the presidential election in the United States in 2020 desire a detailed statistical analysis by advanced statistical tools, as they were much different from the majority of available prognoses as well as from the presented opinion polls. We perform regression modeling for explaining the election results by means of three demographic predictors for individual 50 states: weekly attendance at religious services, percentage of Afroamerican population, and population density. We compare the performance of beta regression with linear regression, while beta regression performs only slightly better in terms of predicting the response. Because the United States population is very heterogeneous and the regression models are heteroscedastic, we focus on regression quantiles in the linear regression model. Particularly, we develop an original quintile regression map, such graphical visualization allows to perform an interesting interpretation of the effect of the demographic predictors on the election outcome on the level of individual states.
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Modelling dependence between hydrological and meteorological variables measured on several stations
Turčičová, Marie ; Jarušková, Daniela (advisor) ; Hlávka, Zdeněk (referee)
Title: Modelling dependence between hydrological and meteorological variables measured on several stations Author: Bc. Marie Turčičová Department: Department of Probability and Mathematical Statistics Supervisor: Prof. RNDr. Daniela Jarušková CSc., Czech Technical University in Prague, Faculty of Civil Engineering, Department of Mathematics Abstract: The aim of the thesis is to explore the dependence of daily discharge averages of the Opava river on high daily precipitation values in its basin. Three methods are presented that can be used for analyzing the dependence between high values of random variables. Their application on the studied data is also given. First it is the tail-dependence coefficient that measures the dependence between high values of two continuous random variables. The model for the high quantiles of the discharge at a given precipitation value was first determined non-parametrically by quantile regression and then parametrically through the peaks-over-threshold (POT) method. Keywords: extremal dependence, tail-dependence coefficient, quantile regression, peaks over threshold method
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Statistical inference based on saddlepoint approximations
Sabolová, Radka ; Jurečková, Jana (advisor) ; Hlávka, Zdeněk (referee) ; Picek, Jan (referee)
Title: Statistical inference based on saddlepoint approximations Author: Radka Sabolová Abstract: The saddlepoint techniques for M-estimators have proved to be very accurate and robust even for small sample sizes. Based on these results, saddle- point approximations of density of regression quantile and saddlepoint tests on the value of regression quantile were derived, both in parametric and nonpara- metric setup. Among these, a test on the value of regression quantile based on the asymptotic distribution of averaged regression quantiles was also proposed and all these tests were compared in a numerical study to the classical tests. Finally, special case of Kullback-Leibler divergence in exponential family was studied and saddlepoint approximations of the density of maximum likelihood estimator and sufficient statistic were also derived using this divergence. 1
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Regression quantiles
Rusnák, Peter ; Kalina, Jan (advisor) ; Zvára, Karel (referee)
Title: Regression Quantiles Author: Peter Rusnák Department: Department of Probabilty and Mathematical Statistics Supervisor: RNDr. Jan Kalina, Ph.D.,Institute of Computer Science, AS CR Abstract: Quantile regression is a statistical method for specifying dependencies among variables, which was introduced by Koenker a Bassett in 1978. Since that time it has gone through a big development, when its theoretical properties have been under study, and it also has found many practical applications for data processing in variety of fields.While ordinary least-squares regression describes the relationship between one or more covariates X and the conditional mean of a response variable Y given X = x, quantile regression describes the relationship between X and the conditional quantiles of variable Y given X = x. This work contains the theory necessary for understanding relationship between standard and quantile regression and enabling include so received estimates to bigger group of M-estimates. The computation of coefficients for particular covariates is made by using Frisch-Newton algorithm belonging to methods of linear programming. The so-called regression ranks are also obtained as a by-product of this algorithm and we discuss their computational aspects and usage for hypothesis testing.In the second part, we...
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Modeling Conditional Quantiles of Central European Stock Market Returns
Burdová, Diana ; Baruník, Jozef (advisor) ; Krištoufek, Ladislav (referee)
Most of the literature on Value at Risk concentrates on the unconditional nonparametric or parametric approach to VaR estimation and much less on the direct modeling of conditional quantiles. This thesis focuses on the direct conditional VaR modeling, using the flexible quantile regression and hence imposing no restrictions on the return distribution. We apply semiparamet- ric Conditional Autoregressive Value at Risk (CAViaR) models that allow time-variation of the conditional distribution of returns and also different time-variation for different quantiles on four stock price indices: Czech PX, Hungarian BUX, German DAX and U.S. S&P 500. The objective is to inves- tigate how the introduction of dynamics impacts VaR accuracy. The main contribution lies firstly in the primary application of this approach on Cen- tral European stock market and secondly in the fact that we investigate the impact on VaR accuracy during the pre-crisis period and also the period covering the global financial crisis. Our results show that CAViaR models perform very well in describing the evolution of the quantiles, both in abso- lute terms and relative to the benchmark parametric models. Not only do they provide generally a better fit, they are also able to produce accurate forecasts. CAViaR models may be therefore used as a...
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Measuring the Value of a Statistical Life in the Czech Republic: A Hedonic Wage Approach
Špiroch, Jakub ; Havránek, Tomáš (advisor) ; Pertold-Gebicka, Barbara (referee)
To resolve the wage-risk trade off relationship on the labor market in Czech Republic, we introduce multiple hedonic wage regressions. Empirical theory ad- mits an income and age heterogeneity in value of a statistical life (VSL). This thesis employs a quantile regression along with age-dependent non-fatal and fa- tal on-the-job risk rates to estimate the age and income variation in VSL within a unified framework. Our results, based on EU-SILC 2018 data, implicate an inverted-V-shaped development of VSL with respect to age. The estimates of age-VSL peak for workers within the age cohort 42-47 across most real wage quantile levels and once reaching the maximum point the VSL proceeds to de- cline with age. In order to infer any effects of the global pandemic on VSL, we propose a set of novel COVID-19 control variables. Additionally, we annuitize the VSL estimates, which yields the value of a statistical life year (VSLY). The measures of VSLY correspond to the age and income varying trend of VSL. In conclusion, this thesis offers applicable varying VSL estimates across cohorts and wage distribution to policy-makers and respective authorities. JEL Classification J17, J24, J28, J31, J33 Keywords hedonic wage, compensating wage differential, quantile regression, VSL, income elasticity Title Measuring the...
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Statistical inference based on saddlepoint approximations
Sabolová, Radka
Title: Statistical inference based on saddlepoint approximations Author: Radka Sabolová Abstract: The saddlepoint techniques for M-estimators have proved to be very accurate and robust even for small sample sizes. Based on these results, saddle- point approximations of density of regression quantile and saddlepoint tests on the value of regression quantile were derived, both in parametric and nonpara- metric setup. Among these, a test on the value of regression quantile based on the asymptotic distribution of averaged regression quantiles was also proposed and all these tests were compared in a numerical study to the classical tests. Finally, special case of Kullback-Leibler divergence in exponential family was studied and saddlepoint approximations of the density of maximum likelihood estimator and sufficient statistic were also derived using this divergence. 1
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Trading strategies based on estimates of conditional distribution of stock returns
Sedlačík, Adam ; Baruník, Jozef (advisor) ; Vošvrda, Miloslav (referee)
In this thesis, a new trading strategy is proposed. By the help of quantile regression, the conditional distribution functions of stock market returns are estimated. Based on the knowledge of the distribution the strategy produced buying and selling signals which together with a weight function derived from exponential moving averages determines how much and when to buy or sell. The strategy performs better than the market in terms of absolute return and the Sharpe ratio in-sample, but it does not provide satisfactory results out-of-sample.
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