Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outcomes, and unsupervised learning, which finds hidden patterns or intrinsic structures in the input data. In this book, supervised learning techniques (predictive techniques) related to regression will be developed. More specifically, we will go deeper into the multidimensional linear regression models, models in simultaneous equations, Box Jenkins ARIMA models, multivariate time series models, models of limited dependent variable and counting. Logistic models, Probit models, Tobit models, Poisson models, Negative Binomial models, panel data models and non linear models. A wide variety of examples and exercises are developed with the Eviews software.
