O nexo causal entre desenvolvimento financeiro e crescimento econômico vem ganhando destaque na literatura desde o início dos anos 1990. As principais linhas teóricas nessa área buscam demonstrar qual a significância da relação e o sentido da causalidade, se houver. Causalidade unidirecional no sentido do desenvolvimento financeiro para o crescimento econômico, bicausalidade entre ambos, e causalidade reversa, no sentido do crescimento para o desenvolvimento financeiro, são as principais hipóteses testadas nas pesquisas empíricas. O presente trabalho de tese tem por objetivo avaliar o nexo causal entre crédito (como um indicador do desenvolvimento financeiro) e crescimento no setor agropecuário brasileiro. O crédito rural como proporção do PIB agropecuário cresceu substancialmente desde meados da década de 90, passando de 15,44% em 1996 para 65,24% em 2014. Ao longo do período 1969-2014, a razão média anual entre crédito rural e PIB agropecuário foi de 43,87%. No mesmo período, o produto agropecuário cresceu em média 3,76% ao ano. Questiona-se se no mercado rural o crédito causa o crescimento agropecuário, se ocorre causalidade reversa ou se se opera a hipótese de bicausalidade. Para avaliar o nexo causal entre essas duas variáveis econômica foram empregados quatro procedimentos metodológicos: teste de causalidade de Granger em uma representação VAR com a abordagem de Toda e Yamamoto, teste de causalidade de Granger em um modelo FMOLS (Fully Modified OLS), teste de causalidade de Granger em um modelo ARDL (Autoregressive-Distributed Lag) e teste de causalidade de Granger no domínio da frequência, com o uso do método de Breitung e Candelon. Os resultados mostram de forma uniforme a presença de causalidade unidirecional do crédito rural para o crescimento do produto agropecuário. Causalidade reversa, no sentido do crescimento agropecuário para o crédito rural, não foi detectada de forma significativa em nenhum dos quatro métodos empregados. A não detecção de bicausalidade pode ser uma evidência do impacto da forte política de subsídio governamental ao crédito rural. A decisão do Governo quanto ao montante anual de crédito rural disponível a taxas de juros subsidiadas pode estar impedindo que o desempenho do setor, medido pela sua taxa de crescimento, exerça uma influência significativa na dinâmica do crédito rural. Os resultados também abrem a possibilidade a testar a hipótese de exogeneidade do crédito rural, o que seria uma extensão direta dos resultados obtidos. / The finance and growth nexus has gained great attention in economic literature since the early 1990s. The main theoretical lines in this area try to demonstrate if the relationship between financial development and economic growth is significant, and what is the direction of causality, if any. Causality running from financial development to economic growth, bi-causality, and reverse causality, from finance to economic growth, are the main hypotheses tested in empirical research. This thesis aims to assess the causal link between credit (as an indicator of financial development) and growth in Brazilian rural sector. The ratio of rural credit to agricultural GDP has grown substantially since the mid-90s, going from 15.44% in 1996 to 65.24% in 2014. Over the period 1969-2014, the ratio of rural credit to agriculture GDP was 43.87% on average. In the same period, agricultural GDP grew 3.76% on average per year. We question whether there is causality running from rural credit to agricultural growth, if reverse causality occurs or if the bi-causality hypothesis operates in Brazilian rural market. To evaluate the causal link between these two economic variables, four methodological procedures were employed: Granger causality test in a VAR framework using Toda and Yamamoto approach, Granger causality test in a FMOLS (Fully Modified OLS) model, Granger causality test in an ARDL (Autoregressive Distributed-Lag) model and Granger causality test in the frequency domain, using Breitung and Candelon method. The results uniformly show the presence of causality running from rural credit to agricultural growth. Reverse causality, from agricultural growth to rural credit, was not significantly detected in any of the four methods. The failure to detect bi-causality may be an evidence of the impact of government\'s subsidy policy on rural credit. The government\'s decisions on the annual amount of rural credit available at subsidized interest rates may be preventing the sector\'s performance, as measured by its growth rate, to significantly influence the dynamics of rural credit. The results also open the possibility to test the exogeneity hypothesis of rural credit.

nÃo hà / The objective of this work is to identify whether the Wagner Law, which refers to the effect of public spending on economic growth can be observed for the Brazilian reality. According to Wagner, the public sector is able to intervene in the economy to alter its growth trajectory, with such a process promoted by higher expenses. To test the hypothesis Wagner information was collected on the amount of government spending and the Brazilian production between 1909 and 2012, extracted from IPEADATA. The processing of information in order to reproduce the arguments that led to the completion of the verification or not the Wagner Law was based on techniques of time series. The model of Vector Autoregressive (VAR), the analysis of Granger and Cointegration test were used. Still, the tests of Dickey - Fuller and Phillip - Perron test for identification to check the type of behavior that characterizes the best series of public spending and growth were used. The results highlight that both variables have decided not stationary in level, you characterize as well as a process I(1). The impulse response functions showed a positive relationship not spending towards growth and a low explanatory power of the fluctuations of the latter after the first 20 periods, respectively. The test of Granger pointed out that there is a unidirectional relationship between public spending and growth, with the same expenditure towards growth - ie, only growth can affect the spending behavior in the Granger sense. Finally, the cointegration analysis did find arguments in favor of a long-term relationship for the series in question. / O objetivo deste trabalho à identificar se a lei de Wagner, que se remete ao efeito dos gastos pÃblicos sobre o crescimento econÃmico, pode ser observada para a realidade brasileira. Segundo Wagner, o setor pÃblico à capaz de intervir na economia de modo a alterar sua trajetÃria de crescimento, sendo tal processo promovido pela elevaÃÃo das despesas. Para testar a hipÃtese de Wagner foram coletadas informaÃÃes sobre o montante de gastos governamentais e sobre a produÃÃo brasileira entre 1909 e 2012, extraÃdas do IPEADATA. O processamento das informaÃÃes de modo a reproduzir argumentos que levaram a conclusÃo sobre a verificaÃÃo ou nÃo da lei de Wagner foram baseado em tÃcnicas de sÃries temporais. Foram utilizados o modelo de Vetores Auto-Regressivos (VAR), a anÃlise de causalidade de Granger e o teste de CointegraÃÃo. Ainda, foram utilizados os testes de Dickey-Fuller Aumentado e o teste de Phillip-Perron para identificaÃÃo para verificaÃÃo do tipo de comportamento que melhor caracteriza as sÃries do gasto pÃblico e do crescimento. Os resultados destacam que ambas as variÃveis apresentam-se de forma nÃo estacionÃria em nÃvel, caracterizas, assim, como um processo I(1). As funÃÃes de impulso respostas evidenciaram uma relaÃÃo nÃo positiva do gasto para com o crescimento e um baixo poder de explicaÃÃo das flutuaÃÃes desta Ãltima pela primeira apÃs 20 perÃodos, respectivamente. O teste de Granger destacou que existe uma relaÃÃo unidirecional entre gasto pÃblico e crescimento, sendo a mesmo no sentido crescimento-gasto, ou seja, somente o crescimento à capaz de afetar o comportamento dos gastos no sentido de Granger. Por fim, a anÃlise de cointegraÃÃo encontrou argumentos em favor de uma relaÃÃo de longo prazo para as sÃries em questÃo.

Redes complexas aplicadas em sinais de atividade cerebral mostraram a presença de anormais padrões de conectividade em pacientes que sofriam com doenças e outros distúrbios psiquiátricos. Logo, passou-se a cogitar a influência dessas estruturas na causa desses problemas e o que leva ao desenvolvimento desses padrões anormais. Do ponto de vista teórico, vários trabalhos mostram como a topologia de uma rede pode alterar um processo que se sustenta nela, por exemplo o modo como a rede influencia a propagação de falhas de um sistema, a sincronização ou processos de dispersão. Nesse sentido, o objetivo do trabalho é caracterizar as redes funcionais de pacientes durante episódios de crises de epilepsia, fazendo um paralelo entre a estrutura dessas redes e os processos dinâmicos envolvidos na crise, em especial a sincronização. Para isto, dados reais foram analisados e as redes inferidas em um primeiro passo. Depois, simulações de sistemas artificiais usando os parâmetros obtidos das análises, mostram o impacto dessas redes nos processos dinâmicos. Os resultados apontam para estruturas que podem aumentar a sincronização e a influência do modo de acoplamento nesses sistemas. / Complex networks applied to brain activity signals show the presence abnormal of connectivity patterns in patients suffering with diseases and others psychiatric disorders. From this, some authors began to question the influence of these structures in the cause of these problems and how it leads to the development of these abnormal patterns. From a theoretical point of view, several studies show how the topology of a network can change a process that maintains it, for example how a network influences the propagation of a system failure, synchronization or diffusion processes. In this sense, the objective of this study is to characterize the functional networks of patients during episodes of seizures, making a parallel between the structure of these networks and the dynamic processes involved in the epilepsy, in particular the synchronization. For this, real data were analyzed and the inferred networks in a first step. And then, artificial simulations using the parameters obtained from the analysis were employed to show the impact of these networks in dynamic processes. The results indicate structures that can enhance the synchronization and the influence of the coupling mode on these systems.

This paper studies the price fluctuation from 2010 to 2016 of two major assets in China: real estate and stock. Equity price is found to Granger cause stock price while the reverse relationship is significant but less strong. The paper then studies whether the nature of the correlation depends on the type of city under consideration. This is achieved by grouping 25 cities into four city tiers based on their level of economic developments and conducting a linear causality test on each city tier. Housing price in first tier cities is found to be much more significantly correlated with stock price. Larger and more developed cities tend to have a stronger correlation with stock than smaller and less developed ones. In addition, the paper also studies the impact of the Chinese government’s recent home purchase restriction on the relationship between the two asset classes. However, the results are contradictory and are not consistent with expectation. The lack of significant results could be contributed to the inherent limitation of our data, as well as the complicated and sometimes confusing policy announcement mechanisms in China.

<p>The dependence on oil has increased in many nations as a result of increasing industrialization and oil has been the factor of many crises as well as many wars. This paper examines how the price of oil affects the unemployment in Sweden. The case of Sweden is interesting since its politics are very different compared to other industrialized countries when it comes to unemployment and benefits. Our main objective is to see whether a change in the oil price will cause a change in unemployment at a later stage. We perform linear regression analysis relating current changes in the variables and Granger causality tests to conclude if there exists a direct relationship.</p><p>The result we received from our linear regression test on current changes and our Granger causality test showed a relationship between the price of oil and unemployment in Sweden. In the linear regression relating current changes in these variables, a positive relationship was indicated. Due to the fact that some of the coefficient estimates are positive and some are negative in the Granger causality regressions, we can not conclude whether an increase in the price of oil will cause a positive or negative effect on unemployment.</p>

This paper details the process of creating the UNO Graduate thesis film The Veil. Each major step in the filmmaking process is covered: screenwriting, producing, directing, cinematography, sound, production design, costumes, hair and makeup, workflow, editing, color correction, music and post-sound. An evaluation of the filmmaking decisions and the success of the project follows the discussions of the process.

<p>The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.</p>

There seems to be a common understanding nowadays that the economy is nonlinear. Economic theory suggests features that can not be incorporated into linear frameworks, and over the decades a solid body of empirical evidence of nonlinearities in economic time series has been gathered. This thesis consists of four essays that have to do with various forms of nonlinear statistical inference. In the first chapter the problem of determining the number regimes in a threshold autoregressive (TAR) model is considered. Typically, the number of regimes (or thresholds) is assumed unknown and has to be determined from the data. The solution provided in the chapter first uses the smooth transition autoregressive (STAR) model with a fixed and rapid transition to approximate the TAR model. The number of thresholds is then determined using sequential misspecification tests developed for the STAR model.  The main characteristic of the proposed method is that only standard statistical inference is used, as opposed to non-standard inference or computation intensive bootstrap-based methods. In the second chapter a similar idea is employed and the structural break model is approximated with a smoothly time-varying autoregressive model. By making the smooth changes in parameters rapid, the model is able to closely approximate the corresponding model with breaks in the parameter structure. This approximation makes the misspecification tests developed for the STR modelling framework available and they can be used for sequentially determining the number of breaks. Again, the method is computationally simple as all tests rely on standard statistical inference. There exists literature suggesting that business cycle fluctuations affect the pattern of seasonality in macroeconomic series. A question asked in the third chapter is whether other factors such as changes in institutions or technological change may have this effect as well. The time-varying smooth transition autoregressive (TV- STAR) models that can incorporate both types of change are used to model the (possible) changes in seasonal patterns and shed light on the hypothesis that institutional and technological changes (proxied by time) may have a stronger effect on seasonal patterns than business cycle. The TV-STAR testing framework is applied to nine quarterly industrial production series from the G7 countries, Finland and Sweden. These series display strong seasonal patterns and also contain the business cycle fluctuations. The empirical results of the chapter suggest that seasonal patterns in these series have been changing over time and, furthermore, that the business cycle fluctuations do not seem to be the main cause for this change. The last chapter of the thesis considers the possibility of testing for Granger causality in bivariate nonlinear systems when the exact form of the nonlinear relationship between variables is not known. The idea is to linearize the testing problem by approximating the nonlinear system by its Taylor expansion. The expansion is linear in parameters and one gets round the difficulty caused by the unknown functional form of the relationship under investigation. / <p>Diss. Stockholm : Handelshögskolan, 2004</p>

In October 2000 the Initiative for the Integration of Regional Infrastructure in South America (IIRSA) was launched. The purpose of the IIRSA is to improve integration of the South American countries and intraregional trade between them. One of the ultimate goals is to promote sustainable growth. The purpose of this paper is to find out if a better quantity and quality of transport infrastructure increases intraregional trade in South America. It is found that the quantity of transport infrastructure increases intraregional trade. On the other hand, there is no evidence for the quality of transport infrastructure increasing intraregional trade in South America. Furthermore, this paper investigates whether economic growth can be obtained through more trade. In other words, this paper examines if trade causes growth. The results do not confirm the trade-growth causality for all countries. The difference between the existence of a trade-growth causal relationship or not could be explained by the core commodities that the different South American countries export.

The purpose of this paper is to investigate the predictive power of the variables advanced notice of dismissal (layoffs) and vacancies for the unemployment rate. Based on the Box Jenkins Methodology, the paper makes use of Granger causality and out-of-sample tests to compare the forecast performance of a naïve reference model and the two models extended to include either lagged values of layoffs or vacancies. It is shown that layoffs make up a significant leading variable, exhibiting particularly strong predictive power at forecast horizons of 2-6 months. It is also shown that the predictive power of vacancies is more ambiguous. Vacancies constitute a valuable explanatory variable for the unemployment rate, but does not possess the same leading, predictive qualities as layoffs.