Publisher's Synopsis
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 linear models multiple regression with all their problems of identification, estimation and diagnosis. Dynamic models and univariate time series models are also contemplated. Almon, Koyck, Klein, and other dynamic models are developed. An important part of the content is the structural changes and stability in dynamic predictive models, unit roots and co-integration. A great variety of examples and practical exercises solved with the Eviews software are presented.
