Upon completion of the course, students will be able to address a wide range of economic problems using statistical learning and data mining techniques, demonstrating both technical and theoretical skills in the analysis of economic data.
Course Program
Week 1 (Textbook, chapter 1 and 2) Introduction to the Course Presentation of the Available materials Clear Statement of Expected Mutual Requirements Regression and classification problems Trade-off’s in statistical learning Parametric and non parametric methods Supervised vs non supervised;
Week 2 (Textbook, chapters 1 and 2) Introduction to the R meta-language Measuring errors in regression and classification models;
Week 3 (Textbook, chapter 3) Introduction to linear models for regression Model fit and inference Variance estimator;
Week 4 (Textbook, chapter 3) Confidence and prediction intervals Algebraic formalization of multiple regression Global test and block-based test Qualitative predictors and interaction effects;
Week 5 (Textbook, chapter 3) Polynomial regression Violations of model assumption Correlated errors Heteroschedasticity Multicollinearity;
Week 6 (Textbook, chapter 3) Practical examples of linear regression in R Implementation and interpretation Model diagnostics;
Week 7 (Textbook, chapter 4) Classification methods Logistic regression Link function and model fit Linear discriminant analysis Bayes rule and difference with logistic approach;
Week 8 (Textbook, chapter 4) Multiple LDA and Logit Class-specific errors Roc curve Quadratic discriminant analysis Comparison of classification methods;
Week 9 (Textbook, chapter 4) Practical examples of classification in R Implementation and interpretation Model diagnostics;
Week 10 (Textbook, chapters 5 and 6) Resampling methods Validation approaches Bootstrap Model selection;
Week 11 (Textbook, chapter 6) Shrinkage methods Ridge regression Lasso regression Model selection via regularization.
