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Econometric Model of Flat Prices in Brno
Ondroušek, Jakub ; Poláček, Tomáš (referee) ; Luňáček, Jiří (advisor)
The goal of the thesis „Econometric model of flat prices in Brno“ is to create econometric model based on data from housing market. The theoretical part of the thesis defines variables, and use descriptive statistics. The practical part of the thesis deals with creation econometric model and interactive calculator.
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Essays on Model Uncertainty and Model Averaging
Skolkova, Alena ; Jurajda, Štěpán (advisor) ; Lafférs, Lukáš (referee) ; Mikusheva, Anna (referee)
In the first chapter of this dissertation I study the properties of a model averaging estimator with ridge regularization. I propose the ridge-regularized modifications of Mallows model averaging (Hansen, 2007, Econometrica}, 75) and heteroskedasticity-robust Mallows model averaging (Liu and Okui, 2013, The Econometrics Journal, 16) to leverage the capabilities of averaging and ridge regularization simultaneously. Via a simulation study, I examine the finite-sample improvements obtained by replacing least-squares with a ridge regression. Ridge-based model averaging is especially useful when one deals with sets of moderately to highly correlated predictors, because the underlying ridge regression accommodates correlated predictors without blowing up estimation variance. A two-model theoretical example shows that the relative reduction of mean squared error is increasing with the strength of the correlation. I also demonstrate the superiority of the ridge- regularized modifications via empirical examples focused on wages and economic growth. The second chapter focuses on the use of elastic-net regression for instrumental variable estimation. I investigate the relative performance of the lasso and elastic-net estimators for fitting the first-stage as part of IV estimation. Because elastic-net includes...
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Rozdíly ve výši příjmů a starobních důchodů u mužů a žen v ČR
Slanařová, Barbora
This diploma thesis deals with differences in income and pension between men and women in the Czech Republic. The target was to evaluate time series and panel data of income and pension of both sexes and to evaluate the existence of differences by using econometrics methods. From the performed analysis was forecast the future development for next three years and then was created the marketing recommendation for retail store regarding the focus on consumers in the pension in the end.
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Informal Economy: A micro-level analysis
Vu Duc, Cuong ; Levely, Ian Vandemark (advisor) ; Cingl, Lubomír (referee)
This paper analyzes association of informal economy with demographic charac- teristics. The first part introduces the definition and composition of the informal economy and sets the theoretical background. It presents its consequences and causes from different points of view. In the second part, we isolate characteris- tics that predict the propensity to work in the informal economy using the probit model. The work finds that the direction of individual effects matches with find- ings in Latin America reported by Perry et al. (2007). Keywords Informal economy, Shadow economy, South Africa, econometrics
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Econometric Model of Flat Prices in Brno
Ondroušek, Jakub ; Poláček, Tomáš (referee) ; Luňáček, Jiří (advisor)
The goal of the thesis „Econometric model of flat prices in Brno“ is to create econometric model based on data from housing market. The theoretical part of the thesis defines variables, and use descriptive statistics. The practical part of the thesis deals with creation econometric model and interactive calculator.
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Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
Kalina, Jan
The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation coefficient based on the least weighted squares regression and the minimum weighted covariance determinant estimator, where the latter allows to estimate the mean and covariance matrix of multivariate data. New tools are proposed allowing to test hypotheses about these robust estimators or to estimate their variance. The techniques considered in the paper include resampling approaches with or without replacement, i.e. permutation tests, bootstrap variance estimation, and bootstrap confidence intervals. The performance of the newly described tools is illustrated on numerical examples. They reveal the suitability of the robust procedures also for non-contaminated data, as their confidence intervals are not much wider compared to those for standard maximum likelihood estimators. While resampling without replacement turns out to be more suitable for hypothesis testing, bootstrapping with replacement yields reliable confidence intervals but not corresponding hypothesis tests.
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Nonparametric Bootstrap Techniques for Implicitly Weighted Robust Estimators
Kalina, Jan
The paper is devoted to highly robust statistical estimators based on implicit weighting, which have a potential to find econometric applications. Two particular methods include a robust correlation coefficient based on the least weighted squares regression and the minimum weighted covariance determinant estimator, where the latter allows to estimate the mean and covariance matrix of multivariate data. New tools are proposed allowing to test hypotheses about these robust estimators or to estimate their variance. The techniques considered in the paper include resampling approaches with or without replacement, i.e. permutation tests, bootstrap variance estimation, and bootstrap confidence intervals. The performance of the newly described tools is illustrated on numerical examples. They reveal the suitability of the robust procedures also for non-contaminated data, as their confidence intervals are not much wider compared to those for standard maximum likelihood estimators. While resampling without replacement turns out to be more suitable for hypothesis testing, bootstrapping with replacement yields reliable confidence intervals but not corresponding hypothesis tests.
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Can the stock markets predict changes in macroeconomic variables?
Vařeka, Marek ; Krištoufek, Ladislav (advisor) ; Hayat, Arshad (referee)
A bstract There is a consensus in the literature, that the stock market can predict the Gross domestic product on quarterly base or the industrial production, which is good proxy for GDP, on monthly basis and that the causal rela tionship between stock market and GDP should work both ways. However, using Vector autoregression model on US data since 1950, model shows that the stock market can not only predict the Industrial production on monthly basis, but also ISM non-manufacturing index, which is a good proxy for services in the economy. Furthermore I have managed to prove, that the unemployment can be predicted by past realizations of the stock market and managed to explain almost one third of all variations in change in un employment using S&P500 and oil prices during last 20 years. The Granger causality test concluded that stock market does cause the unemployment but not vice versa, at least during last 20 years.
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