He main goal of this Thesis is to introduce a tree- structured model that combines aspects from two methodologies: CART (Classification and Regression Trees) and STR (Smooth Transition Regression). The model is called STR-Tree, The idea is to specify a nonlinear parametric model through the structure of a binary decision tree. The resulting modelo can be analyzed as a smooth transition regression model with multiple regimes. The decisions for splitting the nodes of the tree are entirely based on Lagrange Multipliers tests. An alternative specification that uses cross- validation is also tried. A Monte Carlo Experiment is used to evaluate the performance of the proposed methodology and to compare with other techniques that are commonly used. The results showed that the STRTree model outperformed the traditional CART when specifying the architecture of a simulated tree. Moreover, the use of Lagrange Multipliers tests gave better results than a cross-validation procedure. After applying the model to real datasets, it could be seen that STR-Tree showed superior predictive ability when compared to CART. The idea was extended to time series analysis through an application. In this situation, we call the model as STAR- Tree which is obtained through a binary decision tree that fits first-order autoregressive models for different regimes. The model was fitted to the returns of Euro/Dolar exchange rate time series and then evaluated statistically and financially. Comparing with the naive approach and ARMA methodology, the STAR-Tree was parsimonious and presented statistical performance equivalent to others. The financial results were better than the others.