Análise do investimento direto estrangeiro em Portugal utilizando a metodologia de box-jenkins

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

Estudios de series temporales demográficas mediante modelos ARIMA de Box-Jenkins: una aplicación a series de la ciudad de Rosario, Argentina

Incluye Bibliografía

Previsão estocástica de nível fluviométrico para cidade de Marabá-PA: método de Box-Jenkins

O Município de Marabá- PA, situado na região Amazônica, sudeste do Estado do Pará, sofre anualmente com eventos de enchentes, ocasionados pelo aumento periódico do rio Tocantins e pela situação de vulnerabilidade da população que reside em áreas de risco. A defesa civil estadual e municipal anualmente planeja e prepara equipes para ações de defesa no município. Nesta fase o monitoramento e previsão de eventos de enchentes são importantes. Portanto, com o objetivo de diminuir erros nas previsões hidrológicas para o Município de Marabá, desenvolveu-se um modelo estocástico para previsão de nível do rio Tocantins, baseado na metodologia de Box e Jenkins. Utilizou os dados de níveis diários observados nas estações hidrológicas de Marabá e Carolina e Conceição do Araguaia da Agência Nacional de Águas (ANA), do período de 01/12/ 2008 a 31/03/2011. Efetuou-se o ajustamento de três modelos (M_t, N_t e Y_t), através de diferentes aplicativos estatísticos: o SAS e o Gretl, usando diferentes interpretações do comportamento das séries para gerar as equações dos modelos. A principal diferença entre os aplicativos é que no SAS usa o modelo de função de transferência na modelagem. Realizou-se uma classificação da variabilidade do nível do rio, através da técnica dos Quantis para o período de 1972 a 2011, examinando-se apenas as categorizações de níveis ACIMA e MUITO ACIMA do normal. Para análise de impactos socioeconômicos foram usados os dados das ações da Defesa Civil Estado do Pará nas cheias de 2009 e 2011. Os resultados mostraram que o número de eventos de cheias com níveis MUITO ACIMA do normal, geralmente, podem estar associados a eventos de La Niña. Outro resultado importante: os modelos gerados simularam muito bem o nível do rio para o período de sete dias (01/04/2011 a 07/04/2011). O modelo multivariado N_t (com pequenos erros) representou o comportamento da série original, subestimando os valores reais nos dias 3, 4 e 5 de abril de 2011, com erro máximo de 0,28 no dia 4. O modelo univariado (Y_t) teve bons resultados nas simulações com erros absolutos em torno de 0,12 m. O modelo com menor erro absoluto (0,08m) para o mesmo período foi o modelo M_t, desenvolvido pelo aplicativo SAS, que interpreta a série original como sendo não linear e não estacionária. A análise quantitativa dos impactos fluviométricos, ocorridos nas enchentes de 2009 e 2011 na cidade de Marabá, revelou em média que mais de 4 mil famílias sofrem com estes eventos, implicado em gastos financeiros elevados. Logo, conclui-se que os modelos de previsão de níveis são importantes ferramentas que a Defesa Civil, utiliza no planejamento e preparo de ações preventivas para o município de Marabá.

Previsão da velocidade dos ventos por redes neurais artificiais e arima de box & jenkins

Pós-graduação em Engenharia Elétrica - FEIS

Projection de la mortalité aux âges avancées au Canada : comparaison de trois modèles

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Influence of climate on environmental factors associated with long-term changes in chlorophyll production for the Sacramento-San Joaquin delta and Suisun Bay, California

Long-term changes in chlorophyll production were predicted from environmental variables for the Sacramento and San Joaquin rivers and Suisun Bay using Box-Jenkins transfer function models. Data used for the analyses were collected semimonthly or monthly between 1971 and 1987. Transfer function models developed to describe changes in chlorophyll production over time as a function of environmental variables were characterized by lagged responses and described between 39 and 51 percent of the data variation. Significant correlations between environmental variables and the California climate index (CA SLP) were used to develop a conceptual model of the link between regional climate and estuarine production.

A robotic apparatus that dictates torque fields around joints without affecting inherent joint dynamics

This manuscript describes how motor behaviour researchers who are not at the same time expert roboticists may implement an experimental apparatus, which has the ability to dictate torque fields around a single joint on one limb or single joints on multiple limbs without otherwise interfering with the inherent dynamics of those joints. Such an apparatus expands the exploratory potential of the researcher wherever experimental distinction of factors may necessitate independent control of torque fields around multiple limbs, or the shaping of torque fields of a given joint independently of its plane of motion, or its directional phase within that plane. The apparatus utilizes torque motors. The challenge with torque motors is that they impose added inertia on limbs and thus attenuate joint dynamics. We eliminated this attenuation by establishing an accurate mathematical model of the robotic device using the Box-Jenkins method, and cancelling out its dynamics by employing the inverse of the model as a compensating controller. A direct measure of the remnant inertial torque as experienced by the hand during a 50 s period of wrist oscillations that increased gradually in frequency from 1.0 to 3.8 Hz confirmed that the removal of the inertial effect of the motor was effectively complete.

Analysis of Stochastic Volatility Sequences Generated by Product Autoregressive Models

The classical methods of analysing time series by Box-Jenkins approach assume that the observed series uctuates around changing levels with constant variance. That is, the time series is assumed to be of homoscedastic nature. However, the nancial time series exhibits the presence of heteroscedasticity in the sense that, it possesses non-constant conditional variance given the past observations. So, the analysis of nancial time series, requires the modelling of such variances, which may depend on some time dependent factors or its own past values. This lead to introduction of several classes of models to study the behaviour of nancial time series. See Taylor (1986), Tsay (2005), Rachev et al. (2007). The class of models, used to describe the evolution of conditional variances is referred to as stochastic volatility modelsThe stochastic models available to analyse the conditional variances, are based on either normal or log-normal distributions. One of the objectives of the present study is to explore the possibility of employing some non-Gaussian distributions to model the volatility sequences and then study the behaviour of the resulting return series. This lead us to work on the related problem of statistical inference, which is the main contribution of the thesis

Impacto de la innovación en el crecimiento de la oferta exportable de bienes no tradicionales en Colombia

El objetivo de este paper es avanzar en la comprensión existente acerca del impacto de la innovación (en este caso entendida como la inversión en actividades de innovación) en las exportaciones no tradicionales. El estudio analiza un conjunto de datos de empresas colombianas que desempeñan sus actividades en los sectores de la Clasificación Industrial Internacional Uniforme – CIIU - durante el periodo del 2005 al 2012. Para esto se usó un modelo de datos panel en el cual a través de la teoría Box Jenkins, se lograron identificar las variables estadísticamente significativas en el desempeño de las exportaciones. Los hallazgos permiten comprobar las teorías acerca de la relación positiva entre estas variables, y en nuestro caso particular demostrar el impacto que tienen las actividades de innovación en el desarrollo de las exportaciones. Finalmente los resultados sugieren que el estímulo de la innovación y políticas que la promuevan es esencial para el crecimiento de las exportaciones.

Statistical analysis of eight surface ozone measurement series for various sites in Ireland

Data from various stations having different measurement record periods between 1988 and 2007 are analyzed to investigate the surface ozone concentration, long-term trends, and seasonal changes in and around Ireland. Time series statistical analysis is performed on the monthly mean data using seasonal and trend decomposition procedures and the Box-Jenkins approach (autoregressive integrated moving average). In general, ozone concentrations in the Irish region are found to have a negative trend at all sites except at the coastal sites of Mace Head and Valentia. Data from the most polluted Dublin city site have shown a very strong negative trend of −0.33 ppb/yr with a 95% confidence limit of 0.17 ppb/yr (i.e., −0.33 ± 0.17) for the period 2002−2007, and for the site near the city of Cork, the trend is found to be −0.20 ± 0.11 ppb/yr over the same period. The negative trend for other sites is more pronounced when the data span is considered from around the year 2000 to 2007. Rural sites of Wexford and Monaghan have also shown a very strong negative trend of −0.99 ± 0.13 and −0.58 ± 0.12, respectively, for the period 2000−2007. Mace Head, a site that is representative of ozone changes in the air advected from the Atlantic to Europe in the marine planetary boundary layer, has shown a positive trend of about +0.16 ± 0.04 ppb per annum over the entire period 1988−2007, but this positive trend has reduced during recent years (e.g., in the period 2001−2007). Cluster analysis for back trajectories are performed for the stations having a long record of data, Mace Head and Lough Navar. For Mace Head, the northern and western clean air sectors have shown a similar positive trend (+0.17 ± 0.02 ppb/yr for the northern sector and +0.18 ± 0.02 ppb/yr for the western sector) for the whole period, but partial analysis for the clean western sector at Mace Head shows different trends during different time periods with a decrease in the positive trend since 1988 indicating a deceleration in the ozone trend for Atlantic air masses entering Europe.

Assessing macro uncertainty in real-time when data are subject to revision

Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

MODELLING AND FORECASTING INFLATION RATES IN GHANA: AN APPLICATION OF SARIMA MODELS

Ghana faces a macroeconomic problem of inflation for a long period of time. The problem in somehow slows the economic growth in this country. As we all know, inflation is one of the major economic challenges facing most countries in the world especially those in African including Ghana. Therefore, forecasting inflation rates in Ghana becomes very important for its government to design economic strategies or effective monetary policies to combat any unexpected high inflation in this country. This paper studies seasonal autoregressive integrated moving average model to forecast inflation rates in Ghana. Using monthly inflation data from July 1991 to December 2009, we find that ARIMA (1,1,1)(0,0,1)12 can represent the data behavior of inflation rate in Ghana well. Based on the selected model, we forecast seven (7) months inflation rates of Ghana outside the sample period (i.e. from January 2010 to July 2010). The observed inflation rate from January to April which was published by Ghana Statistical Service Department fall within the 95% confidence interval obtained from the designed model. The forecasted results show a decreasing pattern and a turning point of Ghana inflation in the month of July.

The application of an ensemble of boosted elman networks to time series prediction : a benchmark study

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.