Nas últimas três décadas, o Brasil produziu mais de um milhão de mortos por homicídios, alcançando assim a triste posição de 18 país com maior taxa de mortes violentas no mundo (GENEVADECLARATIONON ARMED VIOLENCE AND DEVELOPMENT, 2011). Para solucionar tal problema, diversos esforços privados e públicos foram feitos, tendo sido o Estatuto de Desarmamento um dos esforços de maior destaque. No entanto, apesar de decorridos mais de dez anos após a promulgação desta legislação, a literatura econômica sobre o crime ainda não é unânime acerca dos efeitos das armas de fogo sobre os crimes violentos. Com a intenção de analisar estes efeitos, esta dissertação investiga as diferentes abordagens da Teoria Econômica do Crime e elabora um modelo teórico capaz de respaldar a análise empírica. Esta análise, por sua vez, avalia as relações entre armas de fogo e homicídios por perfuração de arma de fogo no Brasil e no Estado do Rio Grande do Sul, por meio de Vetores Auto Regressivos em painel. Dos resultados obtidos, conclui-se que os efeitos entre armas e homicídios variam de acordo com as heterogeneidades locais, não sendo possível extrapolar os mesmos. / Over the last three decades, Brazil produced over one million deaths by homicide.This puts Brazil in a sad position of the 18thcountry with the highest rates of violent deaths in the world (GENEVA DECLARATION ON ARMED VIOLENCE AND DEVELOPMENT, 2011). To solve this problem, many private and public efforts had been made. One effort with major prominence is the Disarmament Statute. Since the promulgation of this legislation over ten years ago,the economic literature about crimeis still not unanimous about the effectsof firearms on violent crimes. To analyze these effects, this dissertationinvestigates thedifferent approachesof the Economic Theory of Crimeand elaborateson a theoretical modelable to supportthe empirical analysis. This analysisevaluatesthe relations between firearms and homicides byfirearm perforationin Brazilandin the Rio Grande do Sul state, usingpanel Vector Autoregressives. It is concluded thatthe effectsbetweenfirearms and homicides varyaccording tolocal heterogeneities, being impossible to extrapolate this results.

The strong relationship between cognitive processing and coherent behaviour and neurocognitive networks justifies the current huge interest in cortical functional connectivity modeling. This has fostered the development of connectivity estimators from the classical bivariate coherence concept to the notion of multivariate partial directed coherence (PDC) which provides information about temporal dependencies exposing cause and effect relationships. This work examines PDC performance for scalp EEG data whose research value has been subject to much debate in the light of the presence of volume conduction (VC) effects that often obscure the actual nature of cortical source dynamics. Through analytical considerations and simulations we show that even though (VC) can hinder accurate connectivity estimation, one can mitigate its effects by a judicious choice of scalp electrode configuration/ground reference. This observation allows settling the connectivity estimation adequacy debate in the presence of PDC. / A forte relação que processamento cognitivo e comportamento coerente tem com redes neurocognitivas justifica o enorme interesse atual em modelamento de conectividade cortical. Este fato tem justificado o desenvolvimento de estimadores de conectividade desde a clássica coerência bivariada até a noção multivariada de coerência parcial direcionada (PDC) que exibe informação a cerca de dependências temporais que permitem expor relações de causa e efeito. O presente trabalho examina o desempenho da PDC no contexto de EEG de escalpo cujo valor em pesquisa sob os efeitos de condução de volume (VC) tem sido objeto de uma quantidade substancial de questionamentos na medida em esta obscurece a observação da dinâmica das fontes corticais. Por meio de considerações analíticas e simulações, mostramos que é possível mitigar os erros de estimação devidos à VC através da escolha judiciosa da configuração de eletrodos e da referência de terra. Esta observação permite resolver o conflito acerca da adequabilidade da inferência cortical baseada em EEG de escalpo.

nÃo hà / Considerando que a indÃstria brasileira perdeu de forma precoce participaÃÃo no agregado nacional nos Ãltimos anos, este estudo procurou identificar as principais causas desse processo de desindustrializaÃÃo ocorrido no Brasil. Hà duas fontes para tal processo: a primeira tem como base a chamada doenÃa holandesa, que sugere que a apreciaÃÃo da taxa de cÃmbio real, em funÃÃo do aumento das exportaÃÃes das commodities, seria capaz de gerar um efeito negativo sobre a indÃstria como um todo; e a segunda calcada na ideia do custo Brasil, que sugere que o efeito negativo sobre a indÃstria seria ocasionado pelo forte peso de variÃveis como impostos e salÃrios. Nossa proposta à a de investigar o efeito destas variÃveis no processo de desindustrializaÃÃo pÃs-real com base na metodologia de Vetores Autorregressivos â VAR. Nossos resultados sustentam que o custo Brasil à muito mais forte do que a hipÃtese da doenÃa holandesa para explicar o declÃnio da participaÃÃo da indÃstria no PIB. / Considering that the Brazilian industry lost precociously participation in national aggregate in recent years, this study sought to identify the main causes of this deindustrialization occurred in Brazil. There are two sources for such a process: the first is based on the so-called Dutch disease, which suggests that the appreciation of the real exchange rate, due to increased exports of commodities, could have a negative effect on the industry as a whole; and the second grounded in the idea of cost Brazil, which suggests that the negative effect on the industry would be caused by the strong weight of variables such as taxes and wages. Our proposal is to investigate the effect of these variables on the real post deindustrialization process based on the methodology of Vector Autoregressive - VAR. Our results argue that the cost Brazil is much stronger than the Dutch disease hypothesis to explain the decline in the share of industry in GDP.

My thesis will focus on optimal investment decisions, especially those that are planned for longer investment horizon. I will review the literature, showing that changes in investment opportunities can alter the risk-return tradeoff over time and that asset return predictability has an important effect on the variance and correlation structure of returns on bonds, stocks and T bills across investment horizons. The main attention will be given to pension funds, which are institutional investors with relatively long investment horizon. I will find the term structure of risk-return tradeoff in the empirical part of this paper. Later on I will add some variables into the model and investigate whether it can improve the results. Finally the optimal investment strategies will be constructed for various levels of risk tolerance and the results will be compared with strategies of Czech pension funds. I am going to use data from Thomson Reuters Datastream, Wharton Research Data Services and additionally from some other sources.

A agropecuária brasileira tem crescido nas últimas décadas e os ganhos de produtividade tem sido importante neste bom desempenho do setor. O presente trabalho tem dois objetivos principais. O primeiro deles foi mensurar o crescimento desta produtividade total dos fatores na agropecuária brasileira estadual, decompondo o crescimento da PTF em progresso tecnológico e eficiência técnica. O segundo objetivo foi analisar o efeito do crescimento da PTF da agropecuária brasileira sobre os preços agrícolas, no Brasil, de 1970 a 2006. O crescimento desta produtividade foi mensurado a partir dos insumos terra, trabalho e capital na função de produção translog sob orientação do produto, a partir do método de Fronteira Estocástica de Produção e do índice de produtividade de Malmquist. Para avaliar o efeito do crescimento da PTF sobre os preços agrícolas foi construído o índice de preços agrícolas utilizando-se o Índice de preços de Laspeyres para estimar o vetor autoregressivo em painel (panel- VAR), acrescentando as variáveis produtividade total dos fatores (PTF), salário rural, financiamento agrícola e renda per capita domiciliar. Além disso, foi aplicado o teste de causalidade, no sentido de Granger, e estimada a função impulso resposta. A base de dados utilizada foi, obtida do Censo Agropecuário, a nível estadual, para os anos de 1970, 1975, 1980, 1985, 1995 e 2006. Os resultados indicaram que a taxa de crescimento da PTF foi crescente no Brasil e nos estados, sendo que, na maior parte das vezes, é explicada pelo progresso tecnológico, positivo e crescente para todos os estados. A eficiência técnica variou ao longo dos anos, apresentado taxas de crescimento médias positivas para a maioria dos estados. Em média, os estados estiveram situados abaixo da fronteira de produção da agropecuária brasileira. São Paulo foi o estado com maior nível de eficiência técnica. Embora a taxa de crescimento médio anual tenha sido positiva ao longo do período analisado, a eficiência reduziu para todos os estados analisados em 2006. Da análise dos efeitos do crescimento da PTF sobre os preços agrícolas, a PTF tem causalidade, no sentido de Granger, sobre os preços agrícolas. Na função impulso resposta, o choque inicial na variável PTF reduziu os preços nos primeiros anos. Assim, o crescimento da PTF do setor agropecuário contribuiu para o aumento da oferta de produtos, reduzindo os preços agrícolas. A maior disponibilidade de alimentos e, com a redução dos preços dos alimentos, os consumidores, principalmente os de renda mais baixa puderam ter maior acesso aos alimentos. / The Brazilian agriculture has grown in recent decades and productivity gains have been important in this good performance of the sector. This work had two main objectives. The first one was measure the growth of this total factor productivity in agriculture by the Brazilian\'s states, decomposing TFP growth by technological progress, technical efficiency and economies of scale. The second objective was to analyze the effect of TFP growth of Brazilian agriculture on agricultural prices. The growth in productivity was measured from the inputs like labor, gross and capital in the translog production function, from the Stochastic Frontier Analysis and of the outputoriented Malmquist productivity index. To analyze the effect of TFP growth on agricultural prices was constructed an index of agricultural prices through the Laspeyres price index to estimate the vector autoregressive panel (panel-VAR) and establish the relationships between TFP, rural wages, agricultural finance and income per capita household. The Granger causality test and the impulse response function were used to the data panel. The database used obtained from the Agricultural Census, at the state level for the years 1970, 1975, 1980, 1985, 1995 and 2006. The results showed that the growth rate of TFP has been growing in Brazil and in the states, and technological progress explained most of the growth being positive and growing for all states. Technical efficiency varied over the years, presented positive average growth rates for most states. The states were located below the production frontier of Brazilian agriculture and São Paulo was the state with the highest level of technical efficiency. Although the average annual growth rate has been increasing over the period analyzed, the efficiency decreased to all state analyzed in 2006. The results also showed that TFP growth has causality in the sense of Granger, on agricultural prices. In the impulse response function, the initial shock in TFP decreased prices in the early years. Thus, TFP growth of the agricultural sector contributed to the increased supply of agricultural products, reducing agricultural prices. The greater availability of food and with reducing food prices, consumers, especially those from lower income might had greater access to food.

No description available.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

Tourism is currently the third largest industry within South Africa. Many African countries, including South Africa, have the potential to achieve increased economic growth and development with the aid of the tourism sector. As tourism is a great earner of foreign exchange and also creates employment opportunities, especially low–skilled employment, it is identified as a sector that can aid developing countries to increase economic growth and development. Accurate forecasting of tourism demand is important due to the perishable nature of tourism products and services. Little research on forecasting tourism demand in South Africa can be found. The aim of this study is to forecast tourism demand (international tourist arrivals) to South Africa by making use of different causal models and to compare the forecasting accuracy of the causal models used. Accurate forecasts of tourism demand may assist policy–makers and business concerns with decisions regarding future investment and employment. An overview of South African tourism trends indicates that although domestic arrivals surpass foreign arrivals in terms of volume, foreign arrivals spend more in South Africa than domestic tourists. It was also established that tourist arrivals from Africa (including the Middle East), form the largest market of international tourist arrivals to South Africa. Africa is, however, not included in the empirical analysis mainly due to data limitations. All the other markets namely Asia, Australasia, Europe, North America, South America and the United Kingdom are included as origin markets for the empirical analysis and this study therefore focuses on intercontinental tourism demand for South Africa. A review of the literature identified several determinants of tourist arrivals, including income, relative prices, transport cost, climate, supply–side factors, health risks, political stability as well as terrorism and crime. Most researchers used tourist arrivals/departures or tourist spending/receipts as dependent variables in empirical tourism demand studies. The first approach used to forecast tourism demand is a single equation approach, more specifically an Autoregressive Distributed Lag Model. This relationship between the explanatory variables and the dependent variable was then used to ex post forecast tourism demand for South Africa from the six markets identified earlier. Secondly, a system of equation approach, more specifically a Vector Autoregressive Model and Vector Error Correction Model were estimated for each of the identified six markets. An impulse response analysis was undertaken to determine the effect of shocks in the explanatory variables on tourism demand using the Vector Error Correction Model. It was established that it takes on average three years for the effect on tourism demand to disappear. A variance decomposition analysis was also done using the Vector Error Correction Model to determine how each variable affects the percentage forecast variance of a certain variable. It was found that income plays an important role in explaining the percentage forecast variance of almost every variable. The Vector Autoregressive Model was used to estimate the short–run relationship between the variables and to ex post forecast tourism demand to South Africa from the six identified markets. The results showed that enhanced marketing can be done in origin markets with a growing GDP in order to attract more arrivals from those areas due to the high elasticity of the real GDP per capita in the long run and its positive impact on tourist arrivals. It is mainly up to the origin countries to increase their income per capita. Focussing on infrastructure development and maintenance could contribute to an increase in future tourist arrivals. It is evident that arrivals from Europe might have a negative relationship with the number of hotel rooms available since tourists from this region might prefer accommodation with a safari atmosphere such as bush lodges. Investment in such accommodation facilities and the marketing of such facilities to Europeans may contribute to an increase in arrivals from Europe. The real exchange rate also plays a role in the price competitiveness of the destination country. Therefore, in order for South Africa to be more price competitive, inflation rate control can be a way to increase price competitiveness rather than to have a fixed exchange rate. Forecasting accuracy was tested by estimating the Mean Absolute Percentage Error, Root Mean Square Error and Theil’s U of each model. A Seasonal Autoregressive Integrated Moving Average (SARIMA) model was estimated for each origin market as a benchmark model to determine forecasting accuracy against this univariate time series approach. The results showed that the Seasonal Autoregressive Integrated Moving Average model achieved more accurate predictions whereas the Vector Autoregressive model forecasts were more accurate than the Autoregressive Distributed Lag Model forecasts. Policy–makers can use both the SARIMA and VAR model, which may generate more accurate forecast results in order to provide better policy recommendations. / Thesis (M.Com. (Economics))--North-West University, Potchefstroom Campus, 2011.

This paper uses a factor-augmented vector autoregressive model to examine the impact of monetary policy shocks on housing prices across metropolitan and micropolitan regions. To simultaneously estimate the model parameters and unobserved factors we rely on Bayesian estimation and inference. Policy shocks are identified using high-frequency suprises around policy announcements as an external instrument. Impulse reponse functions reveal differences in regional housing price responses, which in some cases are substantial. The heterogeneity in policy responses is found to be significantly related to local regulatory environments and housing supply elasticities. Moreover, housing prices responses tend to be similar within states and adjacent regions in neighboring states. / Series: Working Papers in Regional Science

nÃo hà / Na Ãltima dÃcada, o mercado de crÃdito brasileiro experimentou um crescimento inÃdito na histÃria do paÃs, atingindo o nÃvel de 49% do Produto Interno Bruto. Tal fato està diretamente ligado ao desenvolvimento econÃmico do paÃs nos Ãltimos anos. Diante desse cenÃrio, o preÃo que se cobra nas operaÃÃes de crÃdito passou a ter importÃncia fundamental para a manutenÃÃo de um crescimento sustentÃvel. Nessa perspectiva, os spreads bancÃrios â diferenÃa entre a taxa de juros cobrada dos tomadores de crÃdito e o custo de captaÃÃo dos recursos depositados nas instituiÃÃes financeiras â passaram a ser questionados por conta do elevado nÃvel em que se encontram no Brasil. Esse trabalho se propÃe a analisar a evoluÃÃo do spread bancÃrio brasileiro na Ãltima dÃcada e investigar empiricamente seus determinantes. Para tanto, empregou-se nesta pesquisa a tÃcnica economÃtrica de Vetores Autoregressivos de modo a identificar e analisar as principais variÃveis que se relacionam com o spread no perÃodo de 2000 a 2012. AtravÃs da anÃlise das funÃÃes de Impulso e Resposta, o trabalho mostra que a inflaÃÃo à um dos principais determinantes macroeconÃmicos do spread no Brasil. / The unprecedented growth in the Brazilian credit market in recent years made it possible to reach an impressive level of its GDP. This fact is surely related to economic development experimented by the country in current years. Within this scenario, the price which is charged in credit operations started to play a fundamental role to the maintenance of sustainable growth. Thus, the bank spreads which mean the difference between the interest rate charged to borrowers and the funding cost of funds deposited at financial institutions â also began to be disputed in virtue of their actual high level state. The goal of this work is to evaluate the Brazilian banking spread sector evolution in the last decade and empirically investigate its determinants. Therefore, it may employ tools such as the so-called Vectors Autoregressive in order to figure out and work out the main variables which are related to spread regarding the 2000-2012 period. Making use of impulse-response functions, one intends to show that inflation is one of the main macroeconomic determinants to the Brazilian spread.