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Influence of errors to regression model
Poliačková, Vlasta ; Lachout, Petr (advisor) ; Hlávka, Zdeněk (referee)
Title: Influence of errors to regression model Author: Bc. Vlasta Poliačková Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Petr Lachout, CSc. Supervisor's e-mail address: Petr.Lachout@mff.cuni.cz Abstract: The submitted work deals with the regression model, and the influence of errors to regression. Thesis describes different types of violations of assumptions re- quired to the error term and their impact to the properties of the regression model. In the next part, there are discussed various statistical approaches applicable in the case of violation assumptions of regression model such as heteroscedasticity or autocor- relation of the residuals. In the application part, there is used mainly knowledge of Box - Jenkins methodology. In this section it is described in detail how to build a Box - Jenkins models and forecasts of future values for various real financial time series. In processing of the data are used models of ARMA, ARIMA and SARIMA. In an example, forecasts of the models are compared to real future values of the time series. Keywords: regression, violation of assumptions, error term, Box-Jenkins methodo- logy, time series
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Influence of errors to regression model
Poliačková, Vlasta ; Lachout, Petr (advisor) ; Hlávka, Zdeněk (referee)
Title: Influence of errors to regression model Author: Bc. Vlasta Poliačková Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Petr Lachout, CSc. Supervisor's e-mail address: Petr.Lachout@mff.cuni.cz Abstract: The submitted work deals with the regression model, and the influence of errors to regression. Thesis describes different types of violations of assumptions re- quired to the error term and their impact to the properties of the regression model. In the next part, there are discussed various statistical approaches applicable in the case of violation assumptions of regression model such as heteroscedasticity or autocor- relation of the residuals. In the application part, there is used mainly knowledge of Box - Jenkins methodology. In this section it is described in detail how to build a Box - Jenkins models and forecasts of future values for various real financial time series. In processing of the data are used models of ARMA, ARIMA and SARIMA. In an example, forecasts of the models are compared to real future values of the time series. Keywords: regression, violation of assumptions, error term, Box-Jenkins methodo- logy, time series
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