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Robust estimation of autocorrelation function
Lain, Michal ; Hudecová, Šárka (advisor) ; Hlávka, Zdeněk (referee)
The autocorrelation function is a basic tool for time series analysis. The clas- sical estimation is very sensitive to outliers and can lead to misleading results. This thesis deals with robust estimations of the autocorrelation function, which is more resistant to the outliers than the classical estimation. There are presen- ted following approaches: leaving out the outliers from the data, replacement the average with the median, data transformation, the estimation of another coeffici- ent, robust estimation of the partial autocorrelation function or linear regression. The thesis describes the applicability of the presented methods, their advantages and disadvantages and necessary assumptions. All the approaches are compared in simulation study and applied to real financial data. 1
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L1 Regression
Čelikovská, Klára ; Maciak, Matúš (advisor) ; Hlubinka, Daniel (referee)
This thesis is focused on the L1 regression, a possible alternative to the ordinary least squares regression. L1 regression replaces the least squares estimation with the least absolute deviations estimation, thus generalizing the sample median in the linear regres- sion model. Unlike the ordinary least squares regression, L1 regression enables loosening of certain assumptions and leads to more robust estimates. Fundamental theoretical re- sults, including the asymptotic distribution of regression coefficient estimates, hypothesis testing, confidence intervals and confidence regions, are derived. This method is then compared to the ordinary least squares regression in a simulation study, with a focus on heavy-tailed distributions and the possible presence of outlying observations. 1
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Methods of Robust Econometrics with Applications to Economic Data
Michalíková, Eva ; Víšek, Jan Ámos (advisor) ; Egger, Peter (referee) ; Lachout, Petr (referee) ; Grendár, Marian (referee)
This thesis if focused on the application of methods of robust econometrics to real economic data. We focuse on the issuies of international trade in Czech Republic and the problem of employment and growth of small businesses in Europe. We also focues on estimation of panel data by classical approaches (least squares, fixed effects, GMM) and bzy robust techniques. The first part of dissertation focuses on analyzing determinants of FDI in Czech manufacturing industry. The aim is to estimate a model where the stock of FDI is expressed as a function of several economic factors (K/L, profit per worker, R&D, Balassa index and others). We estimate these models by OLS, fixed effects and GMM. With regard to ambiguous results we used least trimmed squares as a diagnostic tool for detection of outliers. Elimination of two polluting industries out of the data set brings certain improvement in significance of some factors. The second part of dissertation we focus on an estimation of models of employment and net production in 28 European countries for small businesses as a function of economic and institutional variables by special technique of estimation. We describe robust version of within group fixed effects estimation. The aim of paper is to estimate a set of models and to test the properties of estimator. With...
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