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Use of ArcGIS Dashboards to Visualize Covid-19 Pandemic Data
Brezničanová, Adriana
The report deals with the ArcGIS Dashboards application and its use in displaying Covid-19 pandemic data. The subject is a description of the functionality of the application and also its updating depending on the changing data. The paper describes the entire process of creating an application, from creating map layers in ArcGIS Pro, through data processing, publishing them to ArcGIS Online, creating a dashboard and updating it. The result is an approach to the creation of ArcGIS Dashboards and informing about the course of the pandemic on the created dashboard. The resulting dashboard shows the current situation in districts and regions, information on vaccinations, hospital occupancy and also historical data.
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Vizualizácia real-time dát v prostredí virtuálnej reality
Škorňa, Filip
Bachelor thesis deals with visualization of real-time spatial data in a 3D scene presented using the virtual reality technology. The goal of the thesis is to update real-time data from the selected source in the scene automatically without the necessity of user interaction. The key result of this thesis is an Unreal Engine 4 plugin which allows selecting a required data source and particular values from the feed and provides them to the 3D scene using the Blueprint language. The theoretical part of the thesis reviews currently available virtual reality technologies and tools. Follows the review of spatial data visualization in virtual reality. On the basis of the review, the plugin for real-time spatial data is proposed and implemented. Finally, the evaluation of the implementation is provided as well as the proposal for further development of the project.
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Fiscal policy in real-time: Role of growth surprises
Kulichová, Vendula ; Baxa, Jaromír (advisor) ; Hlaváček, Michal (referee)
This thesis explores the reliability of real-time estimates of the cyclically-adjusted primary balances. Using fixed effects and weighted least squares models, we show that the real-time estimates are systematically biased and subsequently revised downwards. Moreover, the most important determinants of the revisions are economic conditions and the cyclically-adjusted primary balance revisions are positively correlated with growth surprises. On the other hand, we do not confirm any significant role of institutions and political environment that has appeared in the previous literature. JEL Classification C23, E62, H68, H87 Keywords Real-time data, fiscal surveillance, Stability and Growth Pact, cyclically-adjusted primary balance Author's e-mail 15883947@fsv.cuni.cz Supervisor's e-mail jaromir.baxa@fsv.cuni.cz
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Real-time versus revised data in estimating the Taylor rule for the Czech Republic
Beňo, David ; Potužák, Pavel (advisor) ; Slaný, Martin (referee)
The main task of this paper is to analyze the differences between estimates of Taylor rule in real-time and with revised data. Estimates of the Taylor rule for the Czech Republic are compared. The source of data is OECD real-time database. The analysis shows that the estimates in real-time and ex-post vary significantly. The average deviation is equal to 0.9 percentage points. The main cause is the estimation of the output gap in real-time. Parameters of estimated reaction function also depend on the type of used data. The rule of inflation targeting or natural growth is more suitable for the use in real-time.
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Forecasting Czech GDP Using Mixed-Frequency Data Models
Franta, Michal ; Havrlant, David ; Rusnák, Marek
In this paper we use a battery of various mixed-frequency data models to forecast Czech GDP. The models employed are mixed-frequency vector autoregressions, mixed-data sampling models, and the dynamic factor model. Using a dataset of historical vintages of unrevised macroeconomic and financial data, we evaluate the performance of these models over the 2005–2012 period and compare them with the Czech National Bank’s macroeconomic forecasts. The results suggest that for shorter forecasting horizons the accuracy of the dynamic factor model is comparable to the CNB forecasts. At longer horizons, mixed-frequency vector autoregressions are able to perform similarly or slightly better than the CNB forecasts. Furthermore, moving away from point forecasts, we also explore the potential of density forecasts from Bayesian mixed-frequency vector autoregressions.
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Nowcasting Czech GDP in Real Time
Rusnák, Marek
The prominent measure of the current state of the Czech economy, gross domestic product (GDP), is available only with a significant lag of roughly 70 days. In this paper, we employ a Dynamic Factor Model (DFM) to nowcast Czech GDP in real time. Using multiple vintages of historical data and taking into account the publication lags of various monthly indicators, we evaluate the real-time performance of the DFM over the 2005– 2012 period. The results suggest that the accuracy of model-based nowcasts is comparable to that of the judgmental nowcasts of the Czech National Bank (CNB). Our results also suggest that foreign variables are crucial for the accuracy of the model, while omitting financial and confidence indicators does not worsen the nowcasting performance. Finally, we show how releases of new data can be viewed through the lens of the dynamic factor model.
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