Submitted by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-11-03T12:04:14Z No. of bitstreams: 1 DIS_ANDRE_LOCATELLI_COMPLETO.pdf: 771690 bytes, checksum: fe8fbda3561c48ca1cee72f699327cba (MD5) / Approved for entry into archive by Caroline Xavier (caroline.xavier@pucrs.br) on 2017-11-03T12:04:27Z (GMT) No. of bitstreams: 1 DIS_ANDRE_LOCATELLI_COMPLETO.pdf: 771690 bytes, checksum: fe8fbda3561c48ca1cee72f699327cba (MD5) / Made available in DSpace on 2017-11-03T12:04:36Z (GMT). No. of bitstreams: 1 DIS_ANDRE_LOCATELLI_COMPLETO.pdf: 771690 bytes, checksum: fe8fbda3561c48ca1cee72f699327cba (MD5) Previous issue date: 2017-08-24 / The present dissertation aims, through the theories of economic political cycles, to investigate if they influence the returns and volatility of the Ibovespa, index of the S?o Paulo Stock Exchange. The four main theories dealing with the theme, Traditional Party Theory, Traditional Opportunist Theory, Rational Party Theory and Opportunistic Rational Theory will be addressed. The data used will be the Ibovespa daily returns and the daily returns of the S & P 500, one of the main indices of the North American stock market and that will serve to capture the changes of the external stock market. In order to calculate the influence of economic policy cycles on the returns and volatility of the Ibovespa, the ARCH and GARCH econometric models have been used, which have been widely used in such works and have been shown to be consistent in the estimation of time series. The ARCH model had better results for the estimated model. Four different Dummy variables, each representing a different time period, were tested to determine whether economic policy cycles influenced Ibovespa returns and volatility in those periods. At the 5% significance level, abnormal returns in the periods included in the Dummy variables were not found nor was there statistically significant change in variance in the same periods. At a significance level of 10%, the influence of economic policy cycles on the volatility of the Ibovespa in the period of 180 days, ranging from 12 months to 6 months before the presidential elections, was found. / A presente disserta??o tem como objetivo, atrav?s das teorias de ciclos pol?ticos econ?micos, investigar se os mesmos influenciam nos retornos e na volatilidade do Ibovespa, ?ndice da bolsa de S?o Paulo. Ser?o abordadas as quatro principais teorias que tratam sobre o tema, Teoria Partid?ria Tradicional, Teoria Oportunista Tradicional, Teoria Partid?ria Racional e Teoria Oportunista Racional. Os dados utilizados ser?o os retornos di?rios do Ibovespa e os retornos di?rios do S&P 500, um dos principais ?ndices do mercado acion?rio norte americano e que servir? para captar as mudan?as do mercado acion?rio externo. Para calcular a influ?ncia dos ciclos pol?ticos econ?micos sobre os retornos e a volatilidade do Ibovespa foram utilizados os modelos econom?tricos ARCH e GARCH, que t?m sido amplamente utilizados em trabalhos dessa natureza, e que t?m se demonstrado consistentes na estima??o de s?ries temporais. O modelo ARCH teve melhores resultados para o modelo estimado. Foram testadas quatro diferentes vari?veis Dummy, cada uma representando um per?odo de tempo diferente, para calcular se os ciclos pol?ticos econ?micos influenciavam os retornos e a volatilidade do Ibovespa naqueles per?odos. N?o foram encontrados, ao n?vel de signific?ncia de 5%, retornos anormais nos per?odos englobados pelas vari?veis Dummy nem se observou altera??o da vari?ncia de forma estatisticamente significativa nos mesmos per?odos. A um n?vel de signific?ncia de 10% foi encontrado a influ?ncia dos ciclos pol?ticos econ?micos na volatilidade do Ibovespa no per?odo de 180 dias que compreende entre 12 meses e 6 meses antes das elei??es presidenciais.

Submitted by Edisangela Bastos (edisangela@ufpa.br) on 2013-08-22T12:11:51Z No. of bitstreams: 2 Tese_ModelagemCanaisComunicacoes.pdf: 2013425 bytes, checksum: a3379b4dc3c84233f1cd63503474b7fe (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) / Approved for entry into archive by Ana Rosa Silva(arosa@ufpa.br) on 2013-08-22T13:25:44Z (GMT) No. of bitstreams: 2 Tese_ModelagemCanaisComunicacoes.pdf: 2013425 bytes, checksum: a3379b4dc3c84233f1cd63503474b7fe (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) / Made available in DSpace on 2013-08-22T13:25:44Z (GMT). No. of bitstreams: 2 Tese_ModelagemCanaisComunicacoes.pdf: 2013425 bytes, checksum: a3379b4dc3c84233f1cd63503474b7fe (MD5) license_rdf: 23898 bytes, checksum: e363e809996cf46ada20da1accfcd9c7 (MD5) Previous issue date: 2013-03 / O presente trabalho apresenta os resultados da modelagem de canal de propagação baseado em séries temporais multivariadas com a utilização de dados coletados em campanhas de medição e as principais características da urbanização de onze vias do centro da cidade de Belém-Pa. Modelos de função de transferência foram utilizados para avaliar efeitos na série temporal da potência do sinal recebido (dBm) que foi utilizada como variável resposta e como variáveis explicativas a altura dos prédios e as distâncias entre os prédios. Como nos modelos em séries temporais desconsideram-se as possíveis correlações entre amostras vizinhas, utilizou-se um modelo geoestatístico para se estabelecer a correção do erro deste modelo. Esta fase do trabalho consistiu em um conjunto de procedimentos necessários às técnicas geoestatísticas. Tendo como objetivo a análise em duas dimensões para dados espacialmente distribuídos, no que diz respeito à interpolação de superfícies geradas a partir das mostras georreferenciadas obtidas dos resíduos da potência do sinal recebido calculados com o modelo em séries temporais. Os resultados obtidos com o modelo proposto apresentam um bom desempenho, com erro médio quadrático na ordem de 0,33 dB em relação ao sinal medido, considerando os dados das onze vias do centro urbano da cidade de Belém/Pa. A partir do mapa de distribuição espacial da potência do sinal recebido (dBm), pode se identificar com facilidade as zonas infra ou supra dimensionadas em termos desta variável, isto é, beneficiadas ou prejudicadas com relação a recepção do sinal, o que pode resultar em um maior investimento da operadora (concessionária de telefonia celular móvel) local naquelas regiões onde o sinal é fraco. / This work presents the results of propagation channel modeling, based on multivariate time series models using data collected in measurement campaigns and the main characteristics of urbanization in the city of Belem-PA. Transfer function models were used to evaluate effects on the time series of received signal strength (dBm) which was used as the response variable and as explanatory variables of the height of buildings and distances between buildings. As time series models disregard to the possible correlations between neighboring samples, we used a geostatistical model to establish the correctness of this model error. This phase of the work consisted of a set of procedures necessary to geostatistical techniques. Aiming at the analysis on two dimensions for data spatially distributed, with respect to the interpolation of surfaces generated from georeferenced samples obtained from residues of received signal power computed using the model series. The results obtained with the proposed model showed an excellent performance, with mean square error in the order of 0.33 dB compared to the measured signal, considering the data of the eleven routes from the center of the city of Belém/Pa. From the map of the spatial distribution of the received signal strength (dBm), one can easily identify areas below or above dimensional in terms of this variable, that is benefited or damaged compared with the signal reception, which may result in a greater investment of the local operator (concessionaire mobile phone) in those regions where the signal is weak.

國小教師供需問題是目前教育界中的一個重要問題，教師需求量的預測精確與否，將影響及教育政策的制定。本研究中，我們使用單變量 ARIMA 及類神經網路，以預測台灣地區 1996 到 1998 年之間的國小教師需求量。 研究結果顯示，在預測國小教師數列上，ARIMA 及類神經望陸均有很好的表現。類神經網路的可用範圍寬廣，適於各種複雜的情境，然而就本研究的主要探討對象--國小教師數列而言，以單變數的神經網路便已足夠。如果能選擇適當、具明顯特徵的資料，則網路將有更佳的預測效果。 由於類神經網路具有自我學習、自我調適、及平行處理等優點，因此在發展教師供需預測系統時，除了 ARIMA 之外，類神經網路為另一可行方法。 / The demand for and supply of teachers in elementary schools is an important problem in education administration. An accurate forecast of the number of teachers needs in elementary schools may heavily affect educational policy. In this thesis, we use the univariate time series analysis and Neural Networks to forecast the number of teacher in elementary schools in Taiwan Area during a period from 1996 to 1998. According to the result, both Box-Jenkins model and Neural Network perform well for prediction. Neural Network can be widely used in different circumstance, especially complicated situation. In this research, however, it is enough to predict number of teacher by the univariate neural network. In other word, if selecting suitable data variables, we could obtain better predictable effect by neural network. With the advantages of self-learning, self adaptation, and parallel processing, the Neural Network approach is a promising alternative approach to time series for developing a teacher demand and supply forecasting system.

流浪教師問題是目前教育界中ㄧ重要問題，流浪教師數的預測精準與否，將會影響教育政策的裁定。本研究中，使用多變量門檻自迴歸模式，預測100年度到103年度的流浪教師數量。結果顯示，多變量門檻自迴歸模式較ARIMA模式更能顯現數列的趨勢，對於預測上有極大的幫助。且多變量門檻自迴歸模式的可用範圍很廣，因為一般的時間數列中或多或少都會有結構改變的現象，時間數列的資料普遍存在有非線性現象，且同時受到多個變數影響，此時加入多個外生變數作為考量，更能精準分析資料和做預測。 / The vagabond teachers in elementary schools is an important problem in education administration. An accurate forecast of the number of vagabond teachers in elementary schools may heavily affect educational policy. In this thesis, we use multivariate TAR model analysis to forecast the number of vagabond teachers in elementary schools in Taiwan Area during a period from 100 to 103. According to the result, multivariate TAR model perform well for prediction. Multivariate TAR model can be widely used in different circumstances, especially complicated situation. As far as common time series data is concerned, it has change point or change period occurs.Structural change of a non-linear time series is auniversal phenomenon. Selecting suitable data variables and using exogenous variables to be a threshold, we could obtain better predictable effect by multivariate TAR model.

No description available.

Recently there have been reports that certain physiological data seem to have the properties of long-range correlation and self-similarity. These two properties can be characterized by a long-range dependent parameter d, as well as a self-similar parameter H. In Peng et al (1995), the alteration of long-range correlations with life-threatening pathologies are studied by analyzing the heart rate data of different groups of subjects. The self-similarity properties of two well-known processes, namely the Fractional Brownian Motion (FBM) and the Fractional ARIMA (FARIMA), are of interest to see if it is suitable to be used to model the heart rate data in order to examine the health conditions of some patients. The Embedded Branching Process (EBP) method for estimating parameter $H$ and a goodness of fit test for examining the self-similarity of a process based on the EBP method are proposed in Jones and Shen (2004). In this work, the performance of the goodness of fit test are examined using simulated data from the FBM and FARIMA processes. A modification of the distribution of the test statistics under null hypothesis is proposed and has been modified to be more appropriate. Some simulation comparisons of different estimation methods of the parameter $H$ for some FARIMA processes are also presented and applied to heart rate data obtained from Kaohsiung Veterans General Hospital.

本文採用Box and Jenkins（1976）所提出的ARIMA模型來進行時間序列資料的配適，並引入轉換函數模型，以政府高教支出、就業人數與固定資本形成毛額為輸入變數，國民生產毛額為輸出變數。 實證結果發現，政府高等教育支出在落後5至6期之後，對於經濟成長會有比較顯著的正面效果。可以解釋為目前一般大專院校畢業生需要一至兩年的時間，以適應職場環境，因此以一般大學的4年修業期間來看，再加上社會新鮮人的適應期，則為6年後，政府對高等教育的投資始會對經濟產生助益。而除了政府高等教育支出可作為經濟成長的領先指標外，當期的經濟表現亦受到前一期經濟表現的影響，有顯著正相關。並且在檢視過轉換函數模型的正確性之後，預測未來5期的國民生產毛額仍是呈現一個上升的趨勢。因此，為促進經濟成長，政府對於高等教育的投資是可以思考的方向之一。然而，必須注意的是高等教育體系的投資，會有一段的時間落後。因此，如要提升國家競爭力，對於高等教育的投資，不但不能荒廢，更應該有長久的規劃。

This thesis examines the links between economic time-series innovations and statistical risk factors in the UK stock market using principal components analysis (PCA) and the general-to-specific (Gets) approach to econometric modelling. A multi-factor risk structure for the UK stock market is assumed, and it is found that the use of economic 'news' (innovations), PCA, the Gets approach, and different stock grouping criteria helps to explain the relationships between stock returns and economic variables. The Kalman Filter appears to be more appropriate than first-differencing or ARIMA modelling as a technique for estimating innovations when applying the Gets approach. Different combinations of economic variables appear to underpin the risk structure of stock returns for different sub-samples. Indications of a possible influence of firm size are found in principal components when different stock sorting criteria are used, but more definite conclusions require simultaneous sorting by market value and beta. Overall it appears that the major factor affecting the identification of specific explanatory economic variables across different sub-samples is the general economic context of investment. The influence of firm size on stock returns seems in particular to be highly sensitive to the wider economic context. There is an apparent instability in the economic underpinnings of the risk structure of stock returns (as measured by principal components) that might also be a result of changing economic conditions.

Submitted by Nathália Faria da Silva (nathaliafsilva.ufv@gmail.com) on 2017-07-04T17:58:58Z No. of bitstreams: 1 texto completo.pdf: 538594 bytes, checksum: 6093b581fc640e6c06d18048d80424f2 (MD5) / Made available in DSpace on 2017-07-04T17:58:58Z (GMT). No. of bitstreams: 1 texto completo.pdf: 538594 bytes, checksum: 6093b581fc640e6c06d18048d80424f2 (MD5) Previous issue date: 2001-02-04 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / Esta pesquisa trata da aplicabilidade de modelos de previsão de séries temporais como ferramenta de decisão de compra e venda de contratos futuros da BM&F, em datas próximas ao vencimento. Para fins empíricos, foram consideradas as commodities boi gordo, café e soja. O objetivo geral foi verificar qual modelo fornece as previsões mais precisas para cada série de preços considerada no mercado físico. O objetivo específico foi calcular os retornos médios de cada modelo em operações de compra e venda nos mercados futuros das commodities analisadas, de modo a fornecer um indicativo do potencial ou da limitação de cada um deles. Os modelos estudados foram os de Box & Jenkins (ARIMA), Redes Neurais, Estruturais e Bayesianos. Os dados utilizados corresponderam às cotações semanais de boi gordo, café e soja nos mercados físico e futuro. A discussão se baseou na hipótese de que esses modelos são instrumentos viáveis de auxílio à tomada de decisão por parte de agentes ligados ao agronegócio, reduzindo a incerteza quanto ao comportamento futuro dos preços. A análise foi conduzida, primeiramente, em termos de Erro Percentual de Previsão da série de preços do mercado físico para, em seguida, verificar os retornos em simulações de compra e venda de contratos futuros de cada produto, utilizando-se o Índice Sharpe, além do viés positivo ou negativo dessa média, através da estatística de simetria e do grau de dispersão dos retornos, medido pela curtose da distribuição destes. De modo geral, os resultados indicaram que: a) os modelos de previsão de séries temporais captam, de modo coerente, o padrão de comportamento dos preços analisados; b) há, contudo, diferenças de desempenho preditivo entre os modelos e entre cada mercado; e c) os retornos financeiros se mostraram positivos na maioria dos contratos analisados, indicando o potencial de utilização desses modelos em negociações de contratos para datas próximas ao vencimento, com destaque para operações fundamentadas nas previsões dos Modelos ARIMA e Estruturais. / This research deals with the usefulness of times series forecast models as a tool for buy and sell decisions of the brazilian BM&F future contracts, in dates nearby the expiration. For this purpose, the commodities considered were live cattle, coffee and soybeans. The general objective is to verify which model generates the most accurate forecasts for each price series of the considered commodities in the spot market. The specific objective is to calculate the medium returns of each model in buy and sell operations in each market of the analyzed commodities, in way to provide an indication of the potentials or limitations of each one.The models considered are the Box & Jenkins (ARIMA), Neural Networks, Structural and Bayesians time series models. The data utilized correspond to the weekly quotations of live cattle, coffee and soybeans in the spot and futures markets. The discussion is based on the hypothesis that those models are viable instruments to support decisions of economic agents participating in the agribussiness, reducing the uncertainty related to the future behavior of the spot prices. The analysis is carried out, firstly, in terms of Percentage Forecast Error for the price series in the spot market. Then, it verifies the returns in simulated buy and sell of future contracts of each product, using the Sharpe Index as a tool for comparsion, as well as the symmetry and kurtosis statistics. In general, the results indicate that: a) the time series forecast models capture coherently the pattern of the analyzed prices; b) there is, however, differences of forecast performance among the models and markets; and c) the financial returns are shown positive in most of the analyzed contracts, indicating the potential use of those models in negotiations of contracts for dates close to the expiration, with prominence for operations based in the forecasts of the ARIMA and Structural models.

In recent years, the demand for electricity has increased in households with the use of different appliances. This raises a concern to many developed and developing nations with the demand in immediate increase of electricity. There is a need for consumers or people to track their daily power usage in houses. In Sweden, scarcity of energy resources is faced during the day. So, the responsibility of human to save and control these resources is also important. This research work focuses on a Smart Metering data for distributing the electricity smartly and efficiently to the consumers. The main drawback of previously used traditional meters is that they do not provide information to the consumers, which is accomplished with the help of Smart Meter. A Smart Meter helps consumer to know the information of consumption of electricity for appliances in their respective houses. The aim of this research work is to measure and analyze power consumption using Smart Meter data by conducting case study on various households. In addition of saving electricity, Smart Meter data illustrates the behaviour of consumers in using devices. As power consumption is increasing day by day there should be more focus on understanding consumption patterns i.e. measurement and analysis of consumption over time is required. In case of developing nations, the technology of employing smart electricity meters is still unaware to many common people and electricity utilities. So, there is a large necessity for saving energy by installing these meters. Lowering the energy expenditure by understanding the behavior of consumers and its correlation with electricity spot prices motivated to perform this research. The methodology followed to analyze the outcome of this study is exhibited with the help of a case analysis, ARIMA model using XLSTAT tool and a flattening technique. Based on price evaluation results provided in the research, hypothesis is attained to change the behavior of consumers when they have better control on their habits. This research contributes in measuring the Smart Meter power consumption data in various households and interpretation of the data for hourly measurement could cause consumers to switch consumption to off-peak periods. With the results provided in this research, users can change their behavior when they have better control on their habits. As a result, power consumption patterns of Smart electricity distribution are studied and analyzed, thereby leading to an innovative idea for saving the limited resource of electrical energy. / +91 9908265578