Scuola di Economia e Management
Syllabus
Academic Year 2017/18 First Semester

Learning Objectives
This course is designed to teach methods of data analysis to students whose primary interest is not in econometrics, statistics or mathematics. It purports to show students how to apply econometric techniques in the context of realworld empirical economic and financial problems. It covers most of the tools used in modern econometrics research, e.g. correlation, regression and extensions for timeseries methods. During the course extensive use of real data examples is made and students are involved in handson computer work.
Learning targets
Students will be able to analyze economic data and the relationships among economic variables by using appropriate statistical tools.
Course Content
Descriptive statistics; Correlations; Regression analysis with one explanatory variable; Hypothesis testing; Regression analysis with multiple explanatory variables; Dummy variables as explanatory variables; Heteroskedasticity; Time series analysis and autocorrelation; Stochastic regressors and instrumenat variables.
Course Delivery
Course Evaluation
The assessment will be based on a written final examination. During the course, students will be encouraged to solve theoretical as well as empirical exercises, individually and in small groups.
Syllabus
Session 1 Hours of lesson: 4 Instructor: M. Galeotti  Topics: Introduction to econometrics. Economic questions and data. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 1. 
Session 2 Hours of lesson: 4 Instructor: M. Galeotti  Topics: Introduction to econometrics. Review of probability and statistics. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 23. 
Session 3 Hours of lesson: 4 Instructor: M. Galeotti  Topics: Linear regression model with a single explanatory variable. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4. 
Session 4 Hours of lesson: 4 Instructor: M. Galeotti  Topics: Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5.

Session 5 Hours of lesson: 4 Instructor: M. Manera  Topics: Linear regression model with a single explanatory variable: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4. 
Session 6 Hours of lesson: 4 Instructor: M. Manera  Topics: Linear regression model with a single explanatory variable: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4. 
Session 7 Hours of lesson: 4 Instructor: M. Manera  Topics: Linear regression model with a single explanatory variable: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 4. 
Session 8 Hours of lesson: 4 Instructor: M. Manera  Topics: Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5. 
Session 9 Hours of lesson: 4 Instructor: M. Manera  Topics: Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5. 
Session 10 Hours of lesson: 4 Instructor: M. Manera  Topics: Hypothesis testing and confidence intervals in the context of the linear regression model with a single explanatory variable: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 5.

Session 11 Hours of lesson: 3,5 Instructor: M. Galeotti  Topics: Linear regression model with multiple explanatory variables: hypothesis testing, dummy variables. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 67. 
Session 12 Hours of lesson: 3 Instructor: M. Manera  Topics: Hypothesis testing and dummy variables in the context of the linear regression model with multiple explanatory variables: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson., Chapters 67. 
Session 13 Hours of lesson: 3,5 Instructor: M. Galeotti  Topics: Linear regression model with multiple explanatory variables: heteroskedasticity. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 67. 
Session 14 Hours of lesson: 3,5 Instructor: M. Galeotti  Topics: Linear regression model with multiple explanatory variables: autocorrelation, time series analysis, forecasting. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson; Chapter 14.

Session 15 Hours of lesson: 3,5 Instructor: M. Galeotti  Topics: Linear regression model with multiple explanatory variables: stochastic regressors and instrumental variables. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapter 12. 
Session 16 Hours of lesson: 3 Instructor: M. Manera  Topics: Autocorrelation, heteroskedasticity and instrumental variables: empirical applications. Readings: Stock J.H., Watson M.W. (2015), Introduction to Econometrics, updated third edition, Pearson, Chapters 67, 12, 14.
