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Approximation of spatially-distributed hierarchically organized data
Smejkalová, Veronika ; Žák, Libor (referee) ; Pavlas, Martin (advisor)
The forecast of the waste production is an important information for planning in waste management. The historical data often consists of short time series, therefore traditional prognostic approaches fail. The mathematical model for forecasting of future waste production based on spatially distributed data with hierarchically structure is suggested in this thesis. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The emphasis is on decomposition of extensive model into subtasks, which lead to a simpler implementation. The output of this thesis is tool tested within case study on municipal waste production data in the Czech Republic.
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Real Estate Cycle in the Czech Republic and Office Capitalization Rate Forecasts
Zelenka, Radek ; Streblov, Pavel (advisor) ; Horváth, Roman (referee)
The presented study describes commercial real estate markets with focus on office sector. We identify the capitalization rate (investment yield) as one of the fundamental variables in the commercial property valuation. Based on historical office investment yield observations and various econometric models we predict the office capitalization rate development in the Czech Republic. We use data of the United Kingdom, Ireland and Sweden to identify common yield trend especially with respect to their real estate crises dating in 1990s which indicate similar features to real estate crisis in 2008-2010. As explanatory variables for the econometric models (ARIMA, OLS, VAR) we use financial and macroeconomic variables. We use the OLS models to identify optimal set of explanatory variables, which we than apply in VAR models. On dataset of the comparable countries we compare the fitness of the VAR and ARIMA models, the best variants are used for prediction of the Czech office yield. We then improve our forecasts by implementing exogenous forecasts of macroeconomic variables used in the models. Majority of our predictions forecast a slow decrease of the prime office capitalization factor in next three years (2011 - 2014) in magnitude of 0.25% - 1.25% (to 6.25% - 5.75%).
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Approximation of spatially-distributed hierarchically organized data
Smejkalová, Veronika ; Žák, Libor (referee) ; Pavlas, Martin (advisor)
The forecast of the waste production is an important information for planning in waste management. The historical data often consists of short time series, therefore traditional prognostic approaches fail. The mathematical model for forecasting of future waste production based on spatially distributed data with hierarchically structure is suggested in this thesis. The approach is based on principles of regression analysis with final balance to ensure the compliance of aggregated data values. The selection of the regression function is a part of mathematical model for high-quality description of data trend. In addition, outlier values are cleared, which occur abundantly in the database. The emphasis is on decomposition of extensive model into subtasks, which lead to a simpler implementation. The output of this thesis is tool tested within case study on municipal waste production data in the Czech Republic.
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Real Estate Cycle in the Czech Republic and Office Capitalization Rate Forecasts
Zelenka, Radek ; Streblov, Pavel (advisor) ; Horváth, Roman (referee)
The presented study describes commercial real estate markets with focus on office sector. We identify the capitalization rate (investment yield) as one of the fundamental variables in the commercial property valuation. Based on historical office investment yield observations and various econometric models we predict the office capitalization rate development in the Czech Republic. We use data of the United Kingdom, Ireland and Sweden to identify common yield trend especially with respect to their real estate crises dating in 1990s which indicate similar features to real estate crisis in 2008-2010. As explanatory variables for the econometric models (ARIMA, OLS, VAR) we use financial and macroeconomic variables. We use the OLS models to identify optimal set of explanatory variables, which we than apply in VAR models. On dataset of the comparable countries we compare the fitness of the VAR and ARIMA models, the best variants are used for prediction of the Czech office yield. We then improve our forecasts by implementing exogenous forecasts of macroeconomic variables used in the models. Majority of our predictions forecast a slow decrease of the prime office capitalization factor in next three years (2011 - 2014) in magnitude of 0.25% - 1.25% (to 6.25% - 5.75%).
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Forecasting models of office capitalization rate in the Czech Republic
Zelenka, Radek ; Streblov, Pavel (advisor) ; Horváth, Roman (referee)
The presented study describes commercial real estate markets with focus on office sector. We identify the capitalization rate (investment yield) as one of the fundamental elements in the commercial property valuation. Based on historical office investment yield observations and various econometric models we predict the office capitalization rate development in the Czech Republic. We use data of the United Kingdom, Ireland and Sweden to identify common yield trend especially with respect to their real estate crises in 1990s that embody features similar to the real estate crisis in 2008-2010. As explanatory variables for the econometric models (ARIMA, OLS, VAR) we use financial and macroeconomic variables. We use the OLS models to identify the optimal set of explanatory variables, to be applied in VAR models. On dataset of the comparable countries we compare the goodness of fit of the VAR and ARIMA models. The best variants are then used for the prediction of the Czech office yield. Lastly, we improve our results by implementing exogenous forecasts of macroeconomic variables used in the models. Majority of our predictions forecast a slow decrease of the capitalization rate in next two years (2010-2012) in the magnitude of 0.25% - 1% (to 6.25%-6%).
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Testování kvality predikcí: vyhodnocení modelu g3
Tkáčik, Marcel ; Vozárová, Pavla (advisor) ; Janíčko, Martin (referee)
Recent developments of New Keynesian models attracted many central banks to develop their own DSGE models for policy analysis and forecasting. The aim of this study is to evaluate the quality of the predictions made by the Czech National Bank which developed its own DSGE model and use it as the core forecasting model from July 2008. The quality of the predictions has been evaluted by comparing it with the Ministry of Finance of the Czech Republic and two commercial banks (Česká spořitelna and Komerční banka). Using the econometrical tests for the structural break and time series analysis, it has been concluded that the Czech National Bank experienced significant improvement in its prediction quality when employing the DSGE model, and surpassed the other three institutions. This study suggests that a well-specified DSGE model may enhance the prediction quality of key macroeconomic indicators compared to non-structural models and expert judgment.
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Predictability of wind famrs operated by Amper-Market company
Farda, Aleš ; Rajdl, Kamil ; Štěpánek, Petr
This report contains an analysis of the electric energy production of wind power plants of the company Amper Market a.s. from the point of view of its predictability, with the main objective of determining the potential accuracy of hourly predictions of the power production. To calculate the analyzed predictions a regression forecasting model utilizing wind speed and direction in two height levels is used. The necessary values of these meteorological variables are obtained from three numerical weather prediction models – Aladin, EPS Aladin and IFS. The accuracy of the energy production predictions is optimized regarding the choice of suitable geographical points for obtaining outputs of the numerical models, the combine utilization of the models and individual setting of the regression model for the single power plants. The analysis was performed based on data from the period January 2013 to April 2014.
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