A democratização do acesso ao ensino superior: um estudo sobre o Programa Universidade Para Todos (PROUNI) e sobre o sistema de reservas de cotas de vagas étnico-raciais

Brazil is an extremely unequal country and this inequality has been a permanent characteristic of its economic and social structure. Some scholars generally consider that the economic growth has generated extreme conditions of space and social inequalities, which reveal themselves within Brazil¿s regions, states, rural and urban areas, central and peripheral areas and among its ethnic groups. Such conditions negatively affect the quality of life of the population and will be reflected in the reduction of life expectancy, in the increase of the indexes of infant mortality and illiteracy, amongst other aspects. Education is considered one of the ways to promote the development of a country, however, access to education, specially higher education in Brazil, since it was first implemented, had been limited to a small group of privileged people, the elite of society. Thus, it becomes necessary to extend the access of students to higher education and consequently to generate individuals capable of changing the reality of the place where they live in and as a result, to develop the country. The purpose of this research is to analyze two programs destined to the amplify the access to higher education in Brazil, namely, the University for All Program (ProUni) and the System of Quotas, with the objective to verify at which level their drawings and strategies will allow the democratization of the access to higher education and the reduction of regional inequalities. In order to achieve its objective, the study is initiated with the issue of development and inequalities in Brazil, then it goes through the history of higher education in Brazil and it is finished with the analysis of ProUni and the System of Quotas.

Turismo e desenvolvimento sustentáve l: o caso da Vila do Abraão

This present work has the objective to analyze the perspective of the sustainable development from the promotion of the tourism in Vila do Abraão, Ilha Grande. For this, was available the five dimensions of sustainable ¿ ecological, economic, social, space and cultural ¿ from the own words of the actors involved with the reality of the studied place. The research was applied as a study of case and had as objective to discuss the tourist¿s projects from Vila do Abraão, considered the viability of its sustainable. The discussion wasn¿t restricted to the economical aspects, but also the social-cultural and environment implications caused by tourism. The dialect method was used. Natives, residents, businesspeople, tourist, authorities, association representatives and others, were interviewed. They were questioned about negative and positive aspects from the local tourism, getting the chance to opinion. The segmented arguments of these actors made the base for new knowledge. This present dissertation arrived at some significant conclusions to the problems that represent barriers to the sustainable mentioned above. In conclusion, were set problems related to government body, whose political interest and insufficient recourses compromise the performance.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties for a lack of parsimony, as well as the traditional ones. We suggest a new procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties. In order to compute the fit of each model, we propose an iterative procedure to compute the maximum likelihood estimates of parameters of a VAR model with short-run and long-run restrictions. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank, relative to the commonly used procedure of selecting the lag-length only and then testing for cointegration.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian inflation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in different measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We consider model selection criteria which have data-dependent penalties as well as the traditional ones. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. Our Monte Carlo simulations measure the improvements in forecasting accuracy that can arise from the joint determination of lag-length and rank using our proposed procedure, relative to an unrestricted VAR or a cointegrated VAR estimated by the commonly used procedure of selecting the lag-length only and then testing for cointegration. Two empirical applications forecasting Brazilian in ation and U.S. macroeconomic aggregates growth rates respectively show the usefulness of the model-selection strategy proposed here. The gains in di¤erent measures of forecasting accuracy are substantial, especially for short horizons.

Model selection, estimation and forecasting in VAR models with short-run and long-run restrictions

We study the joint determination of the lag length, the dimension of the cointegrating space and the rank of the matrix of short-run parameters of a vector autoregressive (VAR) model using model selection criteria. We suggest a new two-step model selection procedure which is a hybrid of traditional criteria and criteria with data-dependant penalties and we prove its consistency. A Monte Carlo study explores the finite sample performance of this procedure and evaluates the forecasting accuracy of models selected by this procedure. Two empirical applications confirm the usefulness of the model selection procedure proposed here for forecasting.