Ensaios econométricos sobre a dinâmica o PIB agrícola - Oliveira Junior, José Nilo de (2007)
The thesis entitled "Econometrical assays on the dynamics of the agricultural Gdp" is composed of three papers. The first article is entitled "Brazilian Agricultural Sector: An Analysis of Microregional Convergence" analyzes the process of agricultural micro regional convergence using the Threshold Model in the period 1970 to 1996. The results show the existence of five clubs of convergence: one with a group of richer micro regions; one with a group of poor micro regions and three intermediates groups. The results also show that physical capital is more important than human capital in the explanation of the growth process. The second paper with title "The Brazilian Agricultural Sector: A Application of the Model of Common Cycles in the period os 1990 the 2005" analyzes the behavior of Agricultural Gdp, real exchange rate and the current account balance agricultural in Brazil during the period of 1990 to 2005. Cointegration techniques were used to identify a VAR system with common stochastic trends, and to investigate the system responses to transitories and permanent shocks. The tests had proven the existence of a common trend and two cycles between the variables. Variance decompositions indicates that transiotories shocks accont form most of the short and long run fluctuations in agricultural Gdp. It was found that permanent shocks explain most of the variance of the exchance rate and of the current account balance mainly in the long run. The third paper entitled "Forecast of the Rate of Growth of the Brazilian Agricultural Gdp: An Application of Models of Diffusion Index Linear and Non-Linear". This article applies the linear and non-linear diffusion index model with a threshold effect to forecast, one step ahead, the qyarterly growt rate of Brazilian Agricultural Gdp. These models are composed by common factor which allow a significant reduction in the number of the original explaining variables. After comparing forecast of these two models between themselves and to an AR model, used as benchmark, one comes to the conclusion that the linear model presents a small superiority, in terms of predictive efficiency, in relation to the nonlinear and AR models.