DATA ANALYSIS
Instructional goals
The goal fo this course is to equip students with econometric models and programming tools to handle data analysis.
Intended learning outcomes
Students will be familiar with basic econometric models, and develop skills to apply those models to actual business cases. They will be also trained to statistical programming with R
Course Contents
Main topics will discuss: simple and multiple regression, hypothesis testing, panel data, instrumental variables, big data.
Reference Books
"Introduction to Econometrics-4th Global Edition" Stock, J and M. Watson, Pearson
Teaching Methods
Frontal lectures, theoretical exercises, practical session with R Studio
Assessment Method
Weekly assignments (70%), final written exam (30%)
Thesis assignment criteria
Upon availability
Week 1 Contenuto sessioni on line e on campus
Review af Statistics and Probability
Week 2 Contenuto sessioni on line e on campus
Linear Regression with One Regressor
Week 3 Contenuto sessioni on line e on campus
Regression with a Single Regressor: Hypothesis Tests and Confidence Intervals
Week 4 Contenuto sessioni on line e on campus
Linear Regression with Multiple Regressors
Week 5 Contenuto sessioni on line e on campus
Hypothesis Tests and Confidence Intervals in Multiple Regression
Week 6 Contenuto sessioni on line e on campus
Regression with Panel Data
Week 7 Contenuto sessioni on line e on campus
Regression with a Binary Dependent Variable
Week 8 Contenuto sessioni on line e on campus
Instrumental Variables Regression
Week 9 Contenuto sessioni on line e on campus
Experiments and Quasi-Experiments
Week 10 Contenuto sessioni on line e on campus
Prediction with Many Regressors and Big Data
Week 11 Contenuto sessioni on line e on campus
Introduction to Time Series Regression and Forecasting
Week 12 Contenuto sessioni on line e on campus
Review