The main goal of the dissertation is to introduce a nonlinear multivariate model, which combines the model STVAR (Smooth Transition Vector Autoregressive) with the CART (Classification and Regression Tree) method and use it for generating scenarios and forecasting. The resulting model is a Tree- Structured Vector Autoregressive model with Smooth Transition, called STVARTree, which is based on the concept of multiple regimes, defined by binary tree. The model specification is based on Lagrange Multiplier tests. Thus, the growth of the tree is conditioned on the existence of nonlinearity in the time series, which indicates the node to be split and the corresponding transition variable. In each division, linear parameters are estimated by Multivariate Least Squares, and nonlinear parameters by Non-Linear Least Squares. As a way of checking the STVAR-Tree model, several Monte Carlo experiments were performed in order to see the functionality of both the LM test and the model estimation. Best results were obtained with medium and large samples. Besides, the STVAR-Tree model was applied to Brazilian time series of Rivers Flow and electricity spot price. In the first study, the model was statistically compared to the Periodic Autoregressive (PAR) model and had a much higher performance than the competitor. In the second case, the model comparison was with Neural-Fuzzy Modeling and the STVAR-Tree model won in one of the four series. Adding both the experiments and the two applications results we conclude that the STVARTree model may be applied to solve real problems, having good results.