Submitted by Marcos Souza Ferreira (mferreira@poli.ufrj.br) on 2016-07-27T13:57:40Z No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET.pdf: 405486 bytes, checksum: 54cd37d39ac7269f0a808b0e73addedb (MD5) / Rejected by Renata de Souza Nascimento (renata.souza@fgv.br), reason: Marcos, boa tarde Por gentileza, verificar a numeração das páginas. Está correto, elas aparecerem a partir da Introdução, porém, não deve se iniciar pela página 1. Por exemplo, se a Introdução é na página 11, incluir a partir da página 11. Em seguida submeter novamente o arquivo. Att on 2016-07-28T15:38:39Z (GMT) / Submitted by Marcos Souza Ferreira (mferreira@poli.ufrj.br) on 2016-07-28T16:49:04Z No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET_2.pdf: 684136 bytes, checksum: a1699da4f25b85c408c1bb37a9f00b99 (MD5) / Approved for entry into archive by Renata de Souza Nascimento (renata.souza@fgv.br) on 2016-07-28T16:58:52Z (GMT) No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET_2.pdf: 684136 bytes, checksum: a1699da4f25b85c408c1bb37a9f00b99 (MD5) / Made available in DSpace on 2016-07-28T17:28:22Z (GMT). No. of bitstreams: 1 FERREIRA M - BUBBLE DETECTION IN BRAZILS STOCK MARKET_2.pdf: 684136 bytes, checksum: a1699da4f25b85c408c1bb37a9f00b99 (MD5) Previous issue date: 2016-06-28 / Considering the importance of the proper detection of bubbles in financial markets for policymakers and market agents, we used two techniques described in Diba and Grossman (1988b) and in Phillips, Shi, and Yu (2015) to detect periods of exuberance in the recent history of the Brazillian stock market. First, a simple cointegration test is applied. Secondly, we conducted several augmented, right-tailed Dickey-Fuller tests on rolling windows of data to determine the point in which there’s a structural break and the series loses its stationarity.

abstract: As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to power grid systems that improve power system operational performance in the presence of anticipated events such as wind power ramps. Because of the stochastic, uncontrollable nature of renewable resources, a thorough and accurate characterization of wind activity is necessary to maintain grid stability and reliability. Wind power ramps from an existing wind farm are studied to characterize persistence forecasting errors using extreme value analysis techniques. In addition, a novel metric that quantifies the amount of non-stationarity in time series wind power data was proposed and used in a real-time algorithm to provide a rigorous method that adaptively determines training data for forecasts. Lastly, large swings in generation or load can cause system frequency and tie-line flows to deviate from nominal, so an anticipatory MPC-based secondary control scheme was designed and integrated into an automatic generation control loop to improve the ability of an interconnection to respond to anticipated large events and fluctuations in the power system. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2016

This diploma thesis deals with the analysis of the birth rate and fertility rate in dependence on the economic conditions of the Czech Republic. The aim of this thesis is to analyze relations between selected socio-economic indicators and total fertility rate on the basis of available data and to evaluate changes related to birth and fertility in the Czech Republic, which occurred in the period 1993-2015. The thesis is divided into two main parts - theoretical and analytical. In the theoretical part are described the basic methods of calculating the characteristics for the analysis of the level of birthrate and fertility, as well as the development of selected socio-economic indicators. An important part of the first part of the diploma thesis is specification of used methods within time series. The second part is practically focused on cointegration analysis and subsequent assembly of single-row models from which error correction models were obtained by transformation. These can be used to describe short and long term relationships between time series. Explained variables are aggregate fertility in all assembled models, the explanatory variables are GDP, average gross monthly wage, final consumption expenditure for households, child allowances, parental allowance and household loans per capita.

No description available.

This thesis investigates through simulation why tests of unit root and stationarity occasionally result in different conclusions. The thesis focusses on the KPSS test and the ADF test and both review cases with and without a trend. The goal is to bring additional knowledge of whether one of the tests are more reliable in terms of size and power and when contradictory results occur. The result shows that both KPSS and ADF suffer from low power and size distortion and that the problems persists for the most common time series lengths. Problems particularly arise when the time series contains a trend or is a process with both an autoregressive and a moving average part. There is no clear evidence that one of the tests are superior to the other, it rather depends on sample size, parameter value and type of ARIMA process.

No description available.

Air pollution and meteorological conditions impact COVID-19 mortality rates. This research studied Quito, Ecuador, using Granger causality tests and regression models to investigate the relationship between pollutants, meteorological variables, human mobility, and excess deaths. Results suggested that Mobility as defined by Google Mobility Index, Facebook Isolation Index, in addition to Nitrogen Dioxide, and Sulphur Dioxide significantly impact excess deaths, while Carbon Monoxide and Relative Humidity have mixed results. Measures to reduce Carbon Monoxide emissions and increase humidity levels may mitigate the impact of air pollution on COVID-19 mortality rates. Further research is needed to investigate the impact of pollutants on COVID-19 transmission in other locations. Healthcare decision-makers must monitor and mitigate the impact of pollutants, promote healthy air quality policies, and encourage physical activity in safe environments. They must also consider meteorological conditions and implement measures such as increased ventilation and air conditioning to reduce exposure. Additionally, they must consider human mobility and reduce it to slow the spread of the diseases. Decisionmakers must monitor and track excess deaths during the pandemic to understand the impact of pollutants, meteorological conditions, and human mobility on human health. Public education is critical to raising awareness of air quality and its impact on health. Encouraging individuals to reduce their exposure to pollutants and meteorological conditions can play a critical role in mitigating the impact of air pollution on respiratory health during the pandemic.

No description available.

One of the key concerns in spatial analysis and modelling is to study and analyse similarities or dissimilarities between places over geographical space. However, ”global“ spatial models may fail to identify spatial variations of relationships (spatial heterogeneity) by assuming spatial stationarity of relationships. In many real-life situations spatial variation in relationships possibly exists and the assumption of global stationarity might be highly unrealistic leading to ignorance of a large amount of spatial information. In contrast, local spatial models emphasise differences or dissimilarity over space and focus on identifying spatial variations in relationships. These models allow the parameters of models to vary locally and can provide more useful information on the processes generating the data in different parts of the study area. In this study, a framework for localising spatial interaction models, based on geographically weighted (GW) techniques, has been developed. This framework can help in detecting, visualising and analysing spatial heterogeneity in spatial interaction systems. In order to apply the GW concept to spatial interaction models, we investigate several approaches differing mainly in the way calibration points (flows) are defined and spatial separation (distance) between flows is calculated. As a result, a series of localised geographically weighted spatial interaction (GWSI) models are developed. Using custom-built algorithms and computer code, we apply the GWSI models to a journey-to-work dataset in Switzerland for validation and comparison with the related global models. The results of the model calibrations are visualised using a series of conventional and flow maps along with some matrix visualisations. The comparison of the results indicates that in most cases local GWSI models exhibit an improvement over the global models both in providing more useful local information and also in model performance and goodness-of-fit.

Ce mémoire a pour objectif de déterminer si les précipitations convectives estivales simulées par le modèle régional canadien du climat (MRCC) sont stationnaires ou non à travers le temps. Pour répondre à cette question, nous proposons une méthodologie statistique de type fréquentiste et une de type bayésien. Pour l'approche fréquentiste, nous avons utilisé le contrôle de qualité standard ainsi que le CUSUM afin de déterminer si la moyenne a augmenté à travers les années. Pour l'approche bayésienne, nous avons comparé la distribution a posteriori des précipitations dans le temps. Pour ce faire, nous avons modélisé la densité \emph{a posteriori} d'une période donnée et nous l'avons comparée à la densité a posteriori d'une autre période plus éloignée dans le temps. Pour faire la comparaison, nous avons utilisé une statistique basée sur la distance d'Hellinger, la J-divergence ainsi que la norme L2. Au cours de ce mémoire, nous avons utilisé l'ARL (longueur moyenne de la séquence) pour calibrer et pour comparer chacun de nos outils. Une grande partie de ce mémoire sera donc dédiée à l'étude de l'ARL. Une fois nos outils bien calibrés, nous avons utilisé les simulations pour les comparer. Finalement, nous avons analysé les données du MRCC pour déterminer si elles sont stationnaires ou non. / The main goal of this master's thesis is to find whether the summer convective precipitations simulated by the Canadian Regional Climate Model (CRCM) are stationary over time or not. In order to answer that question, we propose both a frequentist and Bayesian statistical methodology. For the frequentist approach, we used standard quality control and the CUSUM to determine if the mean has increased over the years. For the Bayesian approach, we compared the posterior distributions of the precipitations over time. In order to do the comparison, we used a statistic based on the Hellinger's distance, the J-divergence and the L2 norm. In this master's thesis, we used the ARL (average run length) to calibrate each of our methods. Therefore, a big part of this thesis is about studying the actual property of the ARL. Once our tools are well calibrated, we used the simulation to compare them together. Finally, we studied the data from the CRCM to decide, whether or not, the data are stationary.