Submitted by Maria Medeiros (maria.dilva1@ufcg.edu.br) on 2018-06-08T11:39:56Z No. of bitstreams: 1 JULLIANNA VITÓRIO VIEIRA DE AZEVEDO - DISSERTAÇÃO (PPGRN) 2015.pdf: 2576177 bytes, checksum: a2d2205824cb66d177cc7165ce092601 (MD5) / Made available in DSpace on 2018-06-08T11:39:56Z (GMT). No. of bitstreams: 1 JULLIANNA VITÓRIO VIEIRA DE AZEVEDO - DISSERTAÇÃO (PPGRN) 2015.pdf: 2576177 bytes, checksum: a2d2205824cb66d177cc7165ce092601 (MD5) Previous issue date: 2015-02-27 / Neste trabalho objetivou-se avaliar os efeitos das variações sazonais do clima na incidência de doenças respiratórias por influenza (PI) na população idosa de 65 anos ou mais nas localidades de Campina Grande, Região Metropolitana de João Pessoa (RMJP), Monteiro e Patos. Para isso, foram usados modelos lineares generalizados a partir da regressão de Poisson para relacionar a variável dependente configurada como os registros de internações por causas associadas à influenza e as variáveis independentes (precipitação pluvial, temperatura média do ar e umidade relativa do ar), para análise das relações instituídas pela modelagem foi aplicada a análise de variância ANOVA com nível de significância de 0,05 para determinar que variáveis independentes eram mais significativas na modelagem. Também foram analisados os resíduos gerados pelo ajuste dos modelos no intuito de identificar a distribuição que melhor se ajustasse aos dados. Foi aplicado o teste não-paramétrico de Mann-Kendall para análise de tendência da série temporal de internações por causas associadas a influenza como também o teste de raiz unitária de Dick-Fuller (DF) para análise de estacionariedade. Assim determinada às características da série temporal foi aplicada a metodologia de Box e Jenkins (1976), foi utilizado neste estudo o modelo ARIMA e para avaliação dos modelos autoregressivos gerados aplicou-se os índices penalizadores AIC (Akaike’s Information Criterion) e o BIC (Bayesian Information Criterion). Toda análise estática foi realizada no software R. De forma geral pode-se verificar que os maiores picos de internações por PI ocorrem no outono e inverno. Portanto, esses resultados sugerem uma associação entre o frio e as internações por PI. Na maioria dos municípios em estudo, a elevação das taxas de morbidade por influenza e causas associadas na faixa etária de 65 anos ou mais demonstram uma possível ausência de efeito das campanhas de vacinação. A modelagem estatística se apresentou como alternativa na análise e previsão de casos de internações por PI, contribuindo para políticas públicas, ajudando nas tomadas de decisão evitando desperdícios econômicos e humanos. / This work aimed to evaluate the effects of seasonal climatic variations in the incidence of respiratory diseases by influenza (PI) in the elderly population in the cities of Campina Grande, metropolitan region of João Pessoa (RMJP), Monteiro and Patos. Generalized linear models from the linear Poisson regression to relate the dependent variable set to the records of hospitalizations for causes associated with influenza and the independent variables (rainfall, average air temperature and relative humidity) to analyze the relations established by modeling has been used. Aditionally, was applied ANOVA variance test with a significance level of 0.05 of probability to determine which independent variables is more significant. Also the residual generated by adjusting the models in order to identify the distribution that best fitted the data were analyzed. The nonparametric Mann-Kendall trend analysis for the time series of hospitalizations for causes associated with influenza as well as the unit root test Dick-Fuller (DF) for stationary analysis was applied. Once determined the time series characteristics was applied to the methodology Box and Jenkins (1976), was used in this study ARIMA model and evaluation of the autoregressive models generated applied to the punitive indices AIC (Akaike's Information Criterion) and BIC (Bayesian Information Criterion). All static analysis was performed using R software. In general is possible to identify that the highest peaks of hospitalizations for PI occur in autumn and winter. Therefore, these results suggest an association between the cold and hospitalizations for IP. In most municipalities studied, the increase in morbidity rates for influenza and associated causes aged 65 and over show a possible lack of effect of vaccination campaigns. The statistical model is presented as an alternative in the analysis and prediction of cases of hospitalization due to IP, contributing to public policy, helping in decision-making avoiding economic and human losses.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Usual point and interval forecasting based on the autoregressive integrated moving average models (ARIMA) may not be suitable for modelling variables defined over the interval (0, 1). In fact, such forecasting effect predicted values outside variable domain (0, 1). The construction of the prediction intervals usually assumes (i) normality or asymptotic normality and (ii) knowledge of the parameters. If these assumptions are not fully satisfied, then the nominal coverage of the prediction intervals may not be adequate. In order to address this issue, the beta autoregressive moving average model (βARMA), which is a regarded as a suitable tool for modelling and forecasting values defined over the interval (0, 1), was considered. The goal of the present work is to propose a suit of methods for computing prediction interval linked to the βARMA model. We introduced methods for obtaining approximate prediction intervals based on (i) the normal distribution and (ii) the beta distribution quantiles. We also introduced modifications to the interval with bootstrap prediction errors (BPE) proposed for autoregressive models; and to the BCa intervals proposed for beta regression model. Moreover, based on the quantiles of the predicted values, we proposed percentiles intervals for different types of bootstrapping. The proposed prediction intervals were evaluated according to Monte Carlo simulations. Assessed results indicated that the prediction intervals based on the quantiles of the beta distribution outperformed the discussed non-bootstrapping methods. Despite some variance effects, it offered better coverage rate values. However, the BCa based prediction intervals presented well-balance results in all considered test scenarios. Therefore, the BCa prediction interval was selected as the most reliable one. Empirical evaluations of the proposed methods were applied to two actual time series: (i) the water level of the Cantareira water supply system in São Paulo from January 2003 to August 2015 and (ii) the unemployment rate data in São Paulo from January 1991 to November 2005. / O modelo beta autorregressivo de médias móveis (βARMA) foi recentemente proposto para modelagem e previsão de variáveis contínuas no intervalo (0; 1). As previsões pontuais e intervalares deste tipo de variável, por meio dos tradicionais modelos autorregressivos integrados de médias móveis (ARIMA), podem levar a valores fora do intervalo (0; 1). Ainda, a construção de intervalos de predição para valores futuros usualmente assumem (i) aproximações pela distribuição normal e (ii) parâmetros do modelo conhecidos. Quando estas suposições não são satisfeitas, a probabilidade de cobertura dos intervalos pode ficar abaixo do valor nominal. Como alternativa a este problema, intervalos de predição bootstrap tendem a apresentar coberturas mais acuradas. Neste sentido, o presente trabalho propõe diferentes intervalos de predição para o modelo βARMA. Dois desses intervalos propostos são baseados em aproximações, considerando a distribuição normal e os quantis da distribuição beta. Também são consideradas adaptações dos intervalos de predição EPB, propostos para os modelos autorregressivos, e dos intervalos BCa, propostos para o modelo de regressão beta. São também propostos intervalos percentis com diferentes reamostras bootstrap, baseados nos quantis dos valores previstos. Os intervalos de predição propostos são avaliados por meio de simulações de Monte Carlo. O intervalo baseado nos quantis da distribuição beta foi eleito como o melhor entre os intervalos sem bootstrap, uma vez que não apresentou valores de taxa de cobertura muito distorcidos em diferentes cenários. Porém, ainda apresentou variabilidade no seu comportamento. O intervalo BCa apresentou valores bons e constantes em todas as medidas avaliadas e em todos os cenários considerados. Desta forma, o intervalo BCa foi eleito como o mais confiável. Aplicações em dados dos níveis dos mananciais do sistema de captação e tratamento de água para a Grande São Paulo e das taxas de desemprego na região metropolitana de São Paulo foram consideradas como forma de avaliar empiricamente os métodos propostos.

Cette thèse contribue à l'approche économétrique de la croissance pro-pauvre. Elle présente des apports théoriques et empiriques. En premier lieu, elle présente les différentes définitions, indices et politiques de croissance pro-pauvre proposées dans la littérature théorique. Elle examine également les modèles théoriques et empiriques portant sur les interactions entre distribution du revenu et croissance. Elle montre que les mesures traditionnelles, en plus de leurs caractères partiels, peuvent conduire à des résultats contradictoires. Pour contourner ces limites, cette thèse privilégie l'approche alternative qui consiste à utiliser des modèles économétriques. Cette dernière approche, bien qu'elle présente l'avantage d'inclure l'ensemble des dimensions de la pauvreté, souffre de deux types de biais : le biais de sélection et le biais d'endogeneité. Ces derniers s'expliquent par les limitations inhérentes des données : erreurs de mesures, points aberrants. En outre, les résultats obtenus avec cette approche sont sensibles aux formes fonctionnelles choisies. Ainsi, il y'a de bonnes raisons d'utiliser la régression Gini. Malheureusement, les régressions de type Gini n'existaient qu'en coupe instantanée et en séries temporelles. Ainsi, dans un second temps, cette thèse propose d'étendre la réflexion sur la régression Gini en panel. Elle introduit les estimateurs intragroupes, intergroupes, le test d'existence de l'effet individuel et l'estimateur Aitken Gini. Enfin, cette thèse présente des applications empiriques qui illustrent de façon concrète la robustesse de nos estimateurs. Elle s'intéresse particulièrement aux conséquences de la méthode d'estimation et à la section de l'échantillon. Elle conclut que le processus de croissance favorise la réduction de la pauvreté à condition que les inégalités de revenu soient maîtrisées. Mais aussi, que l'impact de la croissance agricole sur la réduction de la pauvreté varie en fonction du niveau de développement du pays. / This thesis contributes to the econometric approach to pro-poor growth. It presents theoretical and empirical contributions. First, it presents the different definitions, indices and the policies of pro-poor growth proposed in the theoretical literature. It also examines the theoretical and empirical models on the interactions between income distribution and growth. It shows that the traditional measures, in addition to their partial characters, can lead to contradictory results. To avoid these limits this thesis emphasizes the alternative approach by using econometric models. The latter approach, although it has the advantage of including all the dimensions of poverty, suffering from two types of bias: selection bias and bias of endogeneity. These are due to the limitations of the data: measurement error, outliers. In addition, the results obtained with this approach are sensitive to selected functional forms. So, There are good reasons to use the Gini regression. Unfortunately, the Gini regressions existed only cross sectional and time series. Thus, in a second time, this thesis proposes to extend the Gini regression on the panel. It introduces within and between estimators, the individual effect test and the Gini Aitken estimator. Finally, this thesis presents empirical applications that illustrate the robustness of our estimators. She is particularly interested in the consequences of the estimation method and the sample section. It concludes that the growth process promotes poverty reduction when income inequalities are overcome. But also, the impact of agricultural growth on poverty reduction varies depending on the country's level of development.

Nine years have passed since the Fukushima Daiichi nuclear disaster (FDND). The Japanese government has been facing the issue of striking a balance among economy, environment, and social opinions for its energy transition policy. Increasing usages of fossil fuel, natural gas, and coal can fix the energy gap left out by reduced nuclear use and stabilise Japan’s energy supply, ensuring economic growth; however, the measure would increase the global warming potential. This study applies the Fossil fuel supply security index (FFSSI) to quantify the present energy supply security in Japan and presents future scenarios of greenhouse gas emissions (GHGs) based on analysed results from the Linear Regression, Polynomial Regression, and Holt-Winters forecasting models. The driving forces of GHGs are analysed by Kaya identity to show the outlook in Japan. The aim of this study is to present the feasibility of reaching the Japanese government launched “Long-Term Energy Supply and Demand Outlook” for fiscal 2030, under Japan’s current energy supply system for policymaker’s consideration. Compared with other Asian-pacific countries (China, South Korea, Taiwan, etc.), the lacking self- sufficiency energy is the major weakness for Japan’s present energy supply system. Moreover, extrapolations based on several forecasting models indicate that the carbon dioxide emission is expected to increase in the next decade if keep continuing the present structure of the energy supply system.

Nous sommes intéressé par la reconstitution et la prédiction des séries temporelles multivariées à partir des données partiellement observées et/ou agrégées.La motivation du problème vient des applications dans la gestion du réseau électrique.Nous envisageons des outils capables de résoudre le problème d'estimation de plusieurs domaines.Après investiguer le krigeage, qui est une méthode de la litérature de la statistique spatio-temporelle, et une méthode hybride basée sur le clustering des individus, nous proposons un cadre général de reconstitution et de prédiction basé sur la factorisation de matrice nonnégative.Ce cadre prend en compte de manière intrinsèque la corrélation entre les séries temporelles pour réduire drastiquement la dimension de l'espace de paramètres.Une fois que le problématique est formalisé dans ce cadre, nous proposons deux extensions par rapport à l'approche standard.La première extension prend en compte l'autocorrélation temporelle des individus.Cette information supplémentaire permet d'améliorer la précision de la reconstitution.La deuxième extension ajoute une composante de régression dans la factorisation de matrice nonnégative.Celle-ci nous permet d'utiliser dans l'estimation du modèle des variables exogènes liées avec la consommation électrique, ainsi de produire des facteurs plus interprétatbles, et aussi améliorer la reconstitution.De plus, cette méthod nous donne la possibilité d'utiliser la factorisation de matrice nonnégative pour produire des prédictions.Sur le côté théorique, nous nous intéressons à l'identifiabilité du modèle, ainsi qu'à la propriété de la convergence des algorithmes que nous proposons.La performance des méthodes proposées en reconstitution et en prédiction est testé sur plusieurs jeux de données de consommation électrique à niveaux d'agrégation différents. / We are interested in the recovery and prediction of multiple time series from partially observed and/or aggregate data.Motivated by applications in electricity network management, we investigate tools from multiple fields that are able to deal with such data issues.After examining kriging from spatio-temporal statistics and a hybrid method based on the clustering of individuals, we propose a general framework based on nonnegative matrix factorization.This frameworks takes advantage of the intrisic correlation between the multivariate time series to greatly reduce the dimension of the parameter space.Once the estimation problem is formalized in the nonnegative matrix factorization framework, two extensions are proposed to improve the standard approach.The first extension takes into account the individual temporal autocorrelation of each of the time series.This increases the precision of the time series recovery.The second extension adds a regression layer into nonnegative matrix factorization.This allows exogenous variables that are known to be linked with electricity consumption to be used in estimation, hence makes the factors obtained by the method to be more interpretable, and also increases the recovery precision.Moreover, this method makes the method applicable to prediction.We produce a theoretical analysis on the framework which concerns the identifiability of the model and the convergence of the algorithms that are proposed.The performance of proposed methods to recover and forecast time series is tested on several multivariate electricity consumption datasets at different aggregation level.

Cette thèse met en évidence l'aspect asymétrique de l'élasticité du chômage par rapport à la production aux Etats-Unis et en Europe. Une première partie de ce travail empirique revient sur une estimation de la valeur ``authentique'' du coefficient d'Okun corrigé du biais de publication. Nous employons une méta-analyse et nous montrons qu'un aspect important de déformation du coefficient réside dans le choix de la variable endogène. Dans le second chapitre, nous montrons que loi d'Okun implique dans ses fondements un comportement procyclique de la productivité générée par la pratique de la rétention de main d’œuvre. L'économie américaine présente une déformation significative du coefficient d’Okun au cours des récessions et reprises depuis le milieu des années 80, quand la productivité a perdu son caractère procyclique. En Allemagne et en France, à l’inverse, le coefficient d'Okun se déforme peu au cours du cycle. Cette spécificité européenne pourrait venir de la nature des fluctuations macroéconomiques. Ainsi, l'économie allemande enregistre des chocs macroéconomiques avec un fort caractère transitoire et persistant. Néanmoins, le reste des pays européens présentent des chocs de nature permanente. Dans le dernier chapitre, nous montrons que le PIB réel et le chômage peuvent partager une relation de cointégration asymétrique qui semble être associée à une courbe de Phillips asymétrique. / This dissertation aims at study asymmetry of elasticity of unemployment to output in United States and Europe. In the first chapter, we employ a meta-analysis to identify the ``authentic'' value of Okun's law coefficient beyond publication bias. We show that measure of Okun's coefficient depends about the choice of endogenous variable. In the second chapter, it appears that Okun's law implies a labor productivity procyclical as firm practices labor hoarding. According our estimates, Okun's law presents significative evidence of asymmetry during recessions and recoveries especially since the mid-1980s when positive correlation between real GDP and productivity has disappeared. Conversely, in France and Germany, we observe a more stable Okun's coefficient along business cycle. The nature of macroeconomic movements in Europe could potentially explain these findings. Germany supports transitory and persistent movements in real GDP and unemployment. Nevertheless, macroeconomic movements in other European countries are driven by permanents shocks. In last chapter, we investigate asymmetric cointregration in a sample of European countries (France, Germany and United Kingdom), we show that asymmetric cointegration between real GDP and unemployment seems to be linked to an asymmetric Phillip's curve.

Streptococcus pneumoniae est une bactérie cocci gram positif commensale de la flore oropharyngée qui colonise le rhinopharynx de l’Homme et dont près de 100 sérotypes sont connus. Les nourrissons et les jeunes enfants représentent son réservoir principal. Le pneumocoque peut être à l’origine d’infections graves, telles que la méningite, les bactériémies et la pneumonie, et moins graves mais plus courantes comme la sinusite et l’otite moyenne aiguë. Deux vaccins anti-pneumococciques conjugués ont été introduits en France : le PCV7 (couvrant contre 7 sérotypes) en 2003 et le PCV13 (couvrant contre 6 sérotypes supplémentaires) en 2010. L’objectif général de ce travail de thèse est d’évaluer l’impact des politiques vaccinales sur les infections invasives à pneumocoque en France, en s’intéressant principalement aux évolutions temporelles et géographiques des plus graves : les méningites à pneumocoque (MP). Un premier travail a étudié les dynamiques temporelles des MP sur la période 2001–2014 afin d’identifier l’impact de l’introduction des vaccins conjugués. Des techniques statistiques de modélisations adaptées aux séries temporelles ont été utilisées. Les résultats de ce travail retrouvent des effets rapportés dans la littérature : une réduction des MP à sérotypes vaccinaux mais aussi une augmentation des MP dues aux sérotypes non inclus dans le vaccin (phénomène de « remplacement sérotypique »).Par conséquent, le premier bénéfice, à l’échelle de la population générale, de l’introduction de cette vaccination a été observé seulement onze ans après l’introduction du PCV7, et principalement suite à l’introduction du PCV13 en 2010, avec une diminution de 25% du nombre de MP en 2014. La composante géographique a ensuite été prise en compte afin d’étudier le rôle de la de couverture vaccinale dans la variabilité des MP annuelles entre les départements sur la période 2001-2016. Les résultats confirment l’efficacité des deux formulations du vaccin sur les MP dues aux sérotypes vaccinaux et suggèrent une certaine homogénéité de cet effet entre les différents départements. Inversement, le remplacement sérotypique a été confirmé mais uniquement suite à l’introduction de la première formulation du vaccin et ces effets présentent une répartition géographique hétérogène et variable. La variabilité de la couverture vaccinale entre les départements n’explique pas celle observée dans le nombre de MP, ce qui suggère l’intervention d’autres facteurs tel que la densité géographique. Enfin, une modélisation dynamique, permettant de prendre en compte des aspects fondamentaux des dynamiques de transmission et d’infection du pneumocoque non intégrés dans les méthodes de modélisation statique, a été proposée afin de prédire l’impact de différentes stratégies de vaccination pour les adultes de 65 ans et plus et ainsi évaluer leur rapport coût-utilité. / Streptococcus pneumoniae is a Gram-positive commensal bacterium of the oropharyngeal flora usually colonizing human’s rhino pharynx, of which almost 100 serotypes are known. Infants and young children constitute its main reservoir. Pneumococcus may cause serious infections, such as meningitis, bacteremia and pneumonia, or less serious but more common such as sinusitis and acute otitis media (AOM). Two conjugate pneumococcal vaccines have been introduced in France: PCV7 (covering 7 serotypes) in 2003 and PCV13 (covering 6 additional serotypes) in 2010. The overall objective of this thesis is to assess the impact of vaccination policy on invasive pneumococcal diseases in France, by focusing on temporal and geographical trends of the most serious of them: pneumococcal meningitis (PM). An initial study of PMs temporal dynamics over the 2011-2014 period assessed the impact of conjugate vaccines’ introduction. Statistical modeling techniques were used for time series analysis. The results confirm the effects found in literature: a reduction of vaccine serotypes PMs but at the same time an increase of PMs, due to non-vaccine serotypes (effect of “serotype replacement”). Therefore, the first benefit of vaccine introduction at population scale has been observed no less than 11 years after PCV7 introduction, and then principally after PCV13 was introduced in 2010, with a 25% decrease in PMs in 2014. The geographic component was then implemented to analyze the role of vaccine coverage in annual PM variability between geographic units over the 2001-2016 period. Results confirm the effectiveness of both vaccine compositions on vaccine serotypes PMs and suggest homogeneity of this effect among geographic units. Conversely the serotype replacement has been confirmed only after the first vaccine composition was introduced and presents a variable and heterogeneous geographical repartition. Variability in vaccine coverage among geographic units doesn’t explain the differences in PMs, which could suggest the role of others factors such as demographic density. Finally, a dynamic modeling capable of taking into consideration fundamental aspects of pneumococcus transmission and infection mechanisms not integrated in static modeling has been proposed in order to predict the impacts of different vaccination strategies for 65+ adults and therefore assess their cost-utility ratios.

This study aimed to construct a liquidity measure using their proxies and assess their applicability in the financial context. To that, this study proposes the creation of a negotiability measure, which is a compendium of negotiability proxies used by the literature. The statistical procedure used to obtain this measure was the time series factor analysis (TSFA), which it is an extension of traditional factor analysis, working with time series instead of cross-section data. The data used for the illustration presented came from the trading of 858 stocks on BM&FBOVESPA from January 2000 to February 2016. As a result, the measure constructed for the market was demonstrated to be consistent with the others and capable, in terms of correlation, of replacing the proxies used in its construction. In addition, it presented intermediate statistics in relation to their peers, which suggests that the measure can show more balanced results. When analyzed the applicability of the measure in liquidity pricing models, was observed that it has an explanatory power similar to the other proxies used. Having as main differential the advantage of reducing the dimensions of liquidity, considering the information contained in all proxies in only one measure. Moreover, the findings suggest no differences between the means of the measures. However, when observed the variance, the negotiability measure showed distinct from the others, presenting intermediate statistics. In this sense, it is possible to conjecture that the negotiability measure tends to present similar results when used in models based on average, as is the case of regressions. On the other hand, it may be more advantageous and accurate in models that consider variance. / O presente estudo teve o objetivo de construir uma medida de liquidez utilizando suas proxies e avaliar a sua aplicabilidade no contexto financeiro. Para tanto, este trabalho propôs a criação de uma medida de negociabilidade, a qual é um compendio de proxies de negociabilidade empregadas pela literatura. O procedimento estatístico utilizado para a obtenção desta medida foi a Análise Fatorial de Séries Temporais (TSFA), a qual é uma extensão da análise fatorial tradicional, trabalhando com séries de tempo ao invés de dados de corte. Os dados utilizados para a ilustração apresentada foram provenientes da negociação de 858 ações na BM&FBOVESPA no período de janeiro de 2000 até fevereiro de 2016. Como resultados, a medida construída para o mercado demonstrou-se consistente em relação às demais e capaz, em termos de correlação, de substituir as proxies utilizadas na sua construção. Além disso, apresentou estatísticas intermediárias em relação aos seus pares, o que sugere que a medida pode exibir resultados mais equilibrados. Quando analisada a aplicabilidade da medida em modelos de precificação com liquidez, observou-se que ela possui um poder explicativo similar as outras proxies utilizadas. Tem como principal diferencial a vantagem de reduzir as dimensões da liquidez, pois considera a informação contida em todas as proxies em apenas uma medida. Além disso, as descobertas sugeriram não haver diferenças de médias entre as medidas. Porém, quando observada a variância, a medida de negociabilidade se mostrou distinta das demais, apresentando estatísticas intermediárias. Neste sentido, é possível conjecturar que a medida de negociabilidade tende a apresentar resultados similares quando utilizada em modelos baseados em média, como é o caso das regressões. Por outro lado, pode ser mais vantajosa e precisa em modelos que considerem a variância.