15 resultados para Period-cohort Analysis

em Instituto Politécnico do Porto, Portugal


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Introduction: Healthcare improvements have allowed prevention but have also increased life expectancy, resulting in more people being at risk. Our aim was to analyse the separate effects of age, period and cohort on incidence rates by sex in Portugal, 2000–2008. Methods: From the National Hospital Discharge Register, we selected admissions (aged ≥49 years) with hip fractures (ICD9-CM, codes 820.x) caused by low/moderate trauma (falls from standing height or less), readmissions and bone cancer cases. We calculated person-years at risk using population data from Statistics Portugal. To identify period and cohort effects for all ages, we used an age–period–cohort model (1-year intervals) followed by generalised additive models with a negative binomial distribution of the observed incidence rates of hip fractures. Results: There were 77,083 hospital admissions (77.4 % women). Incidence rates increased exponentially with age for both sexes (age effect). Incidence rates fell after 2004 for women and were random for men (period effect). There was a general cohort effect similar in both sexes; risk of hip fracture altered from an increasing trend for those born before 1930 to a decreasing trend following that year. Risk alterations (not statistically significant) coincident with major political and economic change in the history of Portugal were observed around birth cohorts 1920 (stable–increasing), 1940 (decreasing–increasing) and 1950 (increasing–decreasing only among women). Conclusions: Hip fracture risk was higher for those born during major economically/politically unstable periods. Although bone quality reflects lifetime exposure, conditions at birth may determine future risk for hip fractures.

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Dissertação para obtenção do Grau de Mestre em Contabilidade e Finanças Orientadora: Professora Doutora Patrícia Ramos

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Dissertação para obtenção do Grau de Mestre em Contabilidade e Finanças Orientador: Mestre Luis Pereira Gomes

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Dissertação de Mestrado apresentada ao Instituto Superior de Contabilidade e Administração do Porto para a obtenção do grau de Mestre em Auditoria sob orientação de Mestre Helena Maria Santos de Oliveira

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A presente dissertação tem como objetivo analisar se existe relação entre a manipulação de resultados e a qualidade da auditoria, baseado no estudo do comportamento de determinados “accruals” nas empresas portuguesas não cotadas. Nos diversos estudos existentes sobre o tema “Relação da Qualidade da Auditoria e Manipulação de Resultados”, surgem abordados muitos aspetos, nomeadamente no que respeita às motivações, às formas de manipulação e métodos de deteção que se verifica no campo da auditoria e, este trabalho, pretende abordar se o processo da auditoria é, ou não, eficaz na deteção destas práticas efetuadas pelos gestores, pois isso influencia a confiança naqueles que utilizam a informação financeira. Desta forma, o trabalho pretende basear-se nestas abordagens e complementar visões e conclusões. Neste âmbito, surgem perspetivas e informações que alertam para comportamentos de risco, assim como a sua origem, ou seja, as motivações que provocam esta prática, tanto por parte dos gestores como dos administradores. É nesta perspetiva que este trabalho se enquadra, numa sociedade contemporânea que continuadamente dá exemplos reais e concretos destas práticas. Um ponto é comum, que é o facto de a manipulação dos resultados surgir principalmente pelo motivo dos interesses e motivações por parte dos gestores em conseguirem benefícios. Na tese são abordados os incentivos que levam à manipulação no contexto português, que parecem estar relacionados com o contexto económico e fiscal, onde é desenvolvida a atividade dos agentes económicos. Outra abordagem importante no trabalho é a referência às principais metodologias de detenção da manipulação de resultados, nomeadamente os modelos baseados nos accruals e na distribuição de resultados. O modelo empírico deste estudo consiste numa regressão linear múltipla, com o objetivo de explicar a relação, entre a variável accruals discricionários e as variáveis Big4, a dimensão da empresa, o endividamento, o volume de negócios e a rendibilidade. Para complementar este estudo a análise empírica incidiu sobre 4723 empresas portuguesas não cotadas, a amostra usada foi baseada na base de dados SABI, para um período de análise entre 2011 a 2013. Os resultados encontrados sugerem que existe relação entre a qualidade da auditoria e a manipulação dos resultados concluindo que as empresas auditadas pelas Big4 apresentam accruals discricionários inferiores às restantes empresas.

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Seismic data is difficult to analyze and classical mathematical tools reveal strong limitations in exposing hidden relationships between earthquakes. In this paper, we study earthquake phenomena in the perspective of complex systems. Global seismic data, covering the period from 1962 up to 2011 is analyzed. The events, characterized by their magnitude, geographic location and time of occurrence, are divided into groups, either according to the Flinn-Engdahl (F-E) seismic regions of Earth or using a rectangular grid based in latitude and longitude coordinates. Two methods of analysis are considered and compared in this study. In a first method, the distributions of magnitudes are approximated by Gutenberg-Richter (G-R) distributions and the parameters used to reveal the relationships among regions. In the second method, the mutual information is calculated and adopted as a measure of similarity between regions. In both cases, using clustering analysis, visualization maps are generated, providing an intuitive and useful representation of the complex relationships that are present among seismic data. Such relationships might not be perceived on classical geographic maps. Therefore, the generated charts are a valid alternative to other visualization tools, for understanding the global behavior of earthquakes.

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This paper addresses the impact of the CO2 opportunity cost on the wholesale electricity price in the context of the Iberian electricity market (MIBEL), namely on the Portuguese system, for the period corresponding to the Phase II of the European Union Emission Trading Scheme (EU ETS). In the econometric analysis a vector error correction model (VECM) is specified to estimate both long–run equilibrium relations and short–run interactions between the electricity price and the fuel (natural gas and coal) and carbon prices. The model is estimated using daily spot market prices and the four commodities prices are jointly modelled as endogenous variables. Moreover, a set of exogenous variables is incorporated in order to account for the electricity demand conditions (temperature) and the electricity generation mix (quantity of electricity traded according the technology used). The outcomes for the Portuguese electricity system suggest that the dynamic pass–through of carbon prices into electricity prices is strongly significant and a long–run elasticity was estimated (equilibrium relation) that is aligned with studies that have been conducted for other markets.

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Global warming and the associated climate changes are being the subject of intensive research due to their major impact on social, economic and health aspects of the human life. Surface temperature time-series characterise Earth as a slow dynamics spatiotemporal system, evidencing long memory behaviour, typical of fractional order systems. Such phenomena are difficult to model and analyse, demanding for alternative approaches. This paper studies the complex correlations between global temperature time-series using the Multidimensional scaling (MDS) approach. MDS provides a graphical representation of the pattern of climatic similarities between regions around the globe. The similarities are quantified through two mathematical indices that correlate the monthly average temperatures observed in meteorological stations, over a given period of time. Furthermore, time dynamics is analysed by performing the MDS analysis over slices sampling the time series. MDS generates maps describing the stations’ locus in the perspective that, if they are perceived to be similar to each other, then they are placed on the map forming clusters. We show that MDS provides an intuitive and useful visual representation of the complex relationships that are present among temperature time-series, which are not perceived on traditional geographic maps. Moreover, MDS avoids sensitivity to the irregular distribution density of the meteorological stations.

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The goal of this study is to analyze the dynamical properties of financial data series from nineteen worldwide stock market indices (SMI) during the period 1995–2009. SMI reveal a complex behavior that can be explored since it is available a considerable volume of data. In this paper is applied the window Fourier transform and methods of fractional calculus. The results reveal classification patterns typical of fractional order systems.

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The goal of this study is the analysis of the dynamical properties of financial data series from worldwide stock market indexes during the period 2000–2009. We analyze, under a regional criterium, ten main indexes at a daily time horizon. The methods and algorithms that have been explored for the description of dynamical phenomena become an effective background in the analysis of economical data. We start by applying the classical concepts of signal analysis, fractional Fourier transform, and methods of fractional calculus. In a second phase we adopt the multidimensional scaling approach. Stock market indexes are examples of complex interacting systems for which a huge amount of data exists. Therefore, these indexes, viewed from a different perspectives, lead to new classification patterns.

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Portugal, as well as the Mediterranean basin, is favorable to the occurrence of forest fires. In this work a statistical analysis was carried out based on the official information, considering the forest fires occurrences and the corresponding burned area for each of the districts of the mainland Portugal, between 1996 and 2010. Concerning to the forest fires occurrence it was possible to identify three main regions in mainland Portugal, while the burned area can be characterized in two main regions. Associations between districts and years are different in the two approaches. The results obtained provide a synthetic analysis of the phenomenon of forest fires in continental Portugal, based on all the official information available to date.

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Controlled fires in forest areas are frequently used in most Mediterranean countries as a preventive technique to avoid severe wildfires in summer season. In Portugal, this forest management method of fuel mass availability is also used and has shown to be beneficial as annual statistical reports confirm that the decrease of wildfires occurrence have a direct relationship with the controlled fire practice. However prescribed fire can have serious side effects in some forest soil properties. This work shows the changes that occurred in some forest soils properties after a prescribed fire action. The experiments were carried out in soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, Portugal, that had not been burn for four years. The composed soil samples were collected from five plots at three different layers (0-3cm, 3-6cm and 6-18cm) during a three-year monitoring period after the prescribed burning. Principal Component Analysis was used to reach the presented conclusions.

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Forest fires dynamics is often characterized by the absence of a characteristic length-scale, long range correlations in space and time, and long memory, which are features also associated with fractional order systems. In this paper a public domain forest fires catalogue, containing information of events for Portugal, covering the period from 1980 up to 2012, is tackled. The events are modelled as time series of Dirac impulses with amplitude proportional to the burnt area. The time series are viewed as the system output and are interpreted as a manifestation of the system dynamics. In the first phase we use the pseudo phase plane (PPP) technique to describe forest fires dynamics. In the second phase we use multidimensional scaling (MDS) visualization tools. The PPP allows the representation of forest fires dynamics in two-dimensional space, by taking time series representative of the phenomena. The MDS approach generates maps where objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to better understand forest fires behaviour.

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This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.

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This paper analyses forest fires in the perspective of dynamical systems. Forest fires exhibit complex correlations in size, space and time, revealing features often present in complex systems, such as the absence of a characteristic length-scale, or the emergence of long range correlations and persistent memory. This study addresses a public domain forest fires catalogue, containing information of events for Portugal, during the period from 1980 up to 2012. The data is analysed in an annual basis, modelling the occurrences as sequences of Dirac impulses with amplitude proportional to the burnt area. First, we consider mutual information to correlate annual patterns. We use visualization trees, generated by hierarchical clustering algorithms, in order to compare and to extract relationships among the data. Second, we adopt the Multidimensional Scaling (MDS) visualization tool. MDS generates maps where each object corresponds to a point. Objects that are perceived to be similar to each other are placed on the map forming clusters. The results are analysed in order to extract relationships among the data and to identify forest fire patterns.