The aim of the study was to see if any relationship between government spending andunemployment could be empirically found. To test if government spending affectsunemployment, a statistical model was applied on data from Sweden. The data was quarterlydata from the year 1994 until 2012, unit-root test were conducted and the variables wheretransformed to its first-difference so ensure stationarity. This transformation changed thevariables to growth rates. This meant that the interpretation deviated a little from the originalgoal. Other studies reviewed indicate that when government spending increases and/or taxesdecreases output increases. Studies show that unemployment decreases when governmentspending/GDP ratio increases. Some studies also indicated that with an already largegovernment sector increasing the spending it could have negative effect on output. The modelwas a VAR-model with unemployment, output, interest rate, taxes and government spending.Also included in the model were a linear and three quarterly dummies. The model used 7lags. The result was not statistically significant for most lags but indicated that as governmentspending growth rate increases holding everything else constant unemployment growth rateincreases. The result for taxes was even less statistically significant and indicates norelationship with unemployment. Post-estimation test indicates that there were problems withnon-normality in the model. So the results should be interpreted with some scepticism.

The field of time-series analysis has made important contributions to a wide spectrum of applications such as tide-level studies in hydrology, natural resource prospecting in geo-statistics, speech recognition, weather forecasting, financial trading, and economic forecasts and analysis. Nevertheless, the analysis of the non-Gaussian and non-stationary features of time-series remains challenging for the current state-of-art models. This thesis proposes an innovative framework that leverages the theory of copula, combined with a probabilistic framework from the machine learning community, to produce a versatile tool for multiple time-series analysis. I coined this new model Kernel-based Copula Processes (KCPs). Under the new proposed framework, various idiosyncracies can be modeled compactly via a kernel function for each individual time-series, and long-range dependency can be captured by a copula function. The copula function separates the marginal behavior and serial dependency structures, thus allowing them to be modeled separately and with much greater flexibility. Moreover, the codependent structure of a large number of time-series with potentially vastly different characteristics can be captured in a compact and elegant fashion through the notion of a binding copula. This feature allows a highly heterogeneous model to be built, breaking free from the homogeneous limitation of most conventional models. The KCPs have demonstrated superior predictive power when used to forecast a multitude of data sets from meteorological and financial areas. Finally, the versatility of the KCP model is exemplified when it was successfully applied to non-trivial classification problems unaltered.

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Science and Technology / Department of Mathematics

University of Macau / Faculty of Science and Technology / Department of Mathematics

The field of time-series analysis has made important contributions to a wide spectrum of applications such as tide-level studies in hydrology, natural resource prospecting in geo-statistics, speech recognition, weather forecasting, financial trading, and economic forecasts and analysis. Nevertheless, the analysis of the non-Gaussian and non-stationary features of time-series remains challenging for the current state-of-art models. This thesis proposes an innovative framework that leverages the theory of copula, combined with a probabilistic framework from the machine learning community, to produce a versatile tool for multiple time-series analysis. I coined this new model Kernel-based Copula Processes (KCPs). Under the new proposed framework, various idiosyncracies can be modeled compactly via a kernel function for each individual time-series, and long-range dependency can be captured by a copula function. The copula function separates the marginal behavior and serial dependency structures, thus allowing them to be modeled separately and with much greater flexibility. Moreover, the codependent structure of a large number of time-series with potentially vastly different characteristics can be captured in a compact and elegant fashion through the notion of a binding copula. This feature allows a highly heterogeneous model to be built, breaking free from the homogeneous limitation of most conventional models. The KCPs have demonstrated superior predictive power when used to forecast a multitude of data sets from meteorological and financial areas. Finally, the versatility of the KCP model is exemplified when it was successfully applied to non-trivial classification problems unaltered.

Esta investigación se trata de estudiar las contribuciones de las series juveniles de televisión para la formación de la identidad del adolescente. Para esto analizamos la serie española Compañeros, presentada por la cadena privada Antena 3 en el año 2000, después que identificamos que esta serie específica era la favorita de 62 de 100 adolescentes investigados entre 15 y 17 años en Barcelona. El marco teórico se basa en la Psicología; Comunicación Social; Antropología; la Semiología, los Análisis del Discurso, Recepción Activa y ha recurrido a investigaciones recientes sobre el tema.Adicionamos también lecturas e investigaciones sobre Género, porque había que considerar los resultados ya encontrados en investigaciones anteriores a respecto.Las principales conclusiones del trabajo son que las series juveniles de televisión aportan informaciones y contribuyen para la formación de opiniones y actuación social de los adolescentes, que el adolescente remite su interpretación personal como punto de referencia. Pese a que haya coincidencia en la mayoría hay diferencias marcadas en las minorías y interpretaciones individuales. / This thesis investigates the contributions of the juvenile series of television to the development of the identity of teenagers. The theory bases on Psychology, Sociology, Anthropology, Social Communication and also the most recents researches on Active Reception. The research analyses the Spanish juvenile television serial: Compañeros, because that was the favourite program of 100 teenagers (investigated) between 15 to 17 years old in the year of 2000. The teenagers were heard by Group Discussions and by written interviews. They analysed the teenager universe represented on the television program and also compared their own universe with the represented one. They also analysed the characters and commented their identification with themThe conclusions were that the juvenile series of television do contribute with information, formation of opinions, and decisions and actitudes of the teenagers. Although, the reception is critical and personal, but there are some coincidences on the differences of gender.

There is a lack of measured, long-term, reliable, and well-distributed hydrological variables in Canada. These hydrological variables include, but are not limited to: temperature, precipitation, ground runoff, evapotranspiration, soil moisture, and snow water equivalent. The objective of this thesis was to establish the best possible distributed estimates of these hydrological variables for Canada over the period of 1961-2000. The first step was to interpolate measured temperature and precipitation across the country. These interpolated values were then used to calculate the other hydrological variables using the Waterloo Flood Forecasting Model (WATFLOOD). The Waterloo Mapping technique (WATMAP) was developed to use topographic and land cover databases to automatically and systematically derive the information needed to create the drainage database. WATFLOOD was calibrated with the Dynamically Dimensioned Search (DDS) algorithm using the difference between the measured and simulated streamflow as the objective function. After a final calibration of 100 separate DDS runs, distributed time series for the hydrological variables were created. A simple assessment was made for the predictive uncertainty in the simulated streamflow results based on the results of the final calibration. As well, the implications of various climate change scenarios were examined in the context of how they would change the hydrological variables. The major recommendations for future study included: finding other gridded datasets that could be used to verify the ones that were created in this study and examining further the magnitudes of the different kinds of predictive uncertainty (data, model, and parameter). The results of this thesis fit in well with the goals of the study on Predictions in Ungauged Basins. This thesis was organized along the principle of “design the process, not the product”. As such, although a set of final products are presented at the end, the most important part of the thesis was the process that achieved these products. Thus it is not assumed that every technique designed in this thesis will be applicable to every other researcher, but it hoped that most researchers in the field will be able to use at least some parts of the techniques developed here.

There is a lack of measured, long-term, reliable, and well-distributed hydrological variables in Canada. These hydrological variables include, but are not limited to: temperature, precipitation, ground runoff, evapotranspiration, soil moisture, and snow water equivalent. The objective of this thesis was to establish the best possible distributed estimates of these hydrological variables for Canada over the period of 1961-2000. The first step was to interpolate measured temperature and precipitation across the country. These interpolated values were then used to calculate the other hydrological variables using the Waterloo Flood Forecasting Model (WATFLOOD). The Waterloo Mapping technique (WATMAP) was developed to use topographic and land cover databases to automatically and systematically derive the information needed to create the drainage database. WATFLOOD was calibrated with the Dynamically Dimensioned Search (DDS) algorithm using the difference between the measured and simulated streamflow as the objective function. After a final calibration of 100 separate DDS runs, distributed time series for the hydrological variables were created. A simple assessment was made for the predictive uncertainty in the simulated streamflow results based on the results of the final calibration. As well, the implications of various climate change scenarios were examined in the context of how they would change the hydrological variables. The major recommendations for future study included: finding other gridded datasets that could be used to verify the ones that were created in this study and examining further the magnitudes of the different kinds of predictive uncertainty (data, model, and parameter). The results of this thesis fit in well with the goals of the study on Predictions in Ungauged Basins. This thesis was organized along the principle of “design the process, not the product”. As such, although a set of final products are presented at the end, the most important part of the thesis was the process that achieved these products. Thus it is not assumed that every technique designed in this thesis will be applicable to every other researcher, but it hoped that most researchers in the field will be able to use at least some parts of the techniques developed here.

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