Scuola di Economia e Management
Syllabus
Academic Year 2014/15 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
At the end of the course students will be able to approach relatively complex economic and financial problems involving both timeseries and crosssectional data with the support of appropriate statistical and econometric tools (e.g. descriptive statistic, inferential statistics, regression analysis)
Course Content
An overview of econometrics. Introduction to simple linear regression. Statistical aspects of regression. Multiple regression. Relaxation of classical assumptions: autocorrelation, heteroskedasticity, stochastic regressors. Introduction to time series analysis.
Course Delivery
Course Evaluation
Details on the structure of the final exam will be given at the beginning of the course.
Syllabus
Session 1 Hours of lesson: 3 Instructor: M. Galeotti  Topics: An overview of econometrics. A nontechnical introduction to regression. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 1. 
Session 2 Hours of lesson: 3 Instructor: M. Manera  Topics: An overview of econometrics. A nontecnical introduction to regression. Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 1. 
Session 3 Hours of lesson: 3 Instructor: M. Galeotti  Topics: An overview of econometrics. A nontechnical introduction to regression. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 1. 
Session 4 Hours of lesson: 3 Instructor: M. Manera  Topics: The simple linear regression model under classical assumptions: OLS and ttest. Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 23. 
Session 5 Hours of lesson: 3 Instructor: M. Galeotti  Topics: The simple linear regression model under classical assumptions: OLS and ttest. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 23. 
Session 6 Hours of lesson: 3 Instructor: M. Manera  Topics: The simple linear regression model under classical assumptions: OLS and ttest. Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 23. 
Session 7 Hours of lesson: 3 Instructor: M. Galeotti  Topics: The simple linear regression model under classical assumptions: OLS and ttest. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 23. 
Session 8 Hours of lesson: 3 Instructor: M. Manera  Topics: The simple linear regression model under classical assumptions: OLS and ttest. Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 23. 
Session 9 Hours of lesson: 3 Instructor: M. Galeotti  Topics: The simple linear regression model under classical assumptions: OLS and ttest. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 23. 
Session 10 Hours of lesson: 3 Instructor: M. Galeotti  Topics: The multiple linear regression model under classical assumptions: OLS, Ftest, dummy variables, forecasting. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 4. 
Session 11 Hours of lesson: 3 Instructor: M. Manera  Topics: The multiple linear regression model under classical assumptions: OLS, Ftest, dummy variables, forecasting. Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 4. 
Session 12 Hours of lesson: 3 Instructor: M. Galeotti  Topics: The multiple linear regression model under classical assumptions: OLS, Ftest, dummy variables, forecasting. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 4. 
Session 13 Hours of lesson: 3 Instructor: M. Manera  Topics: The multiple linear regression model under classical assumptions: OLS, Ftest, dummy variables, forecasting. Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 4. 
Session 14 Hours of lesson: 3 Instructor: M. Galeotti  Topics: The multiple linear regression model under classical assumptions: OLS, Ftest, dummy variables, forecasting. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 4. 
Session 15 Hours of lesson: 0 Instructor: M. Manera  Topics: The multiple linear regression model under classical assumptions: OLS, Ftest, dummy variables, forecasting. Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 4. 
Session 16 Hours of lesson: 3 Instructor: M. Galeotti  Topics: The multiple linear regression model: relaxation of classical assumptions (autocorrelation, heteroskedasticity, stochastic regressors). Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 5.

Session 17 Hours of lesson: 3 Instructor: M. Manera  Topics: The multiple linear regression model: relaxation of classical assumptions (autocorrelation, heteroskedasticity, stochastic regressors). Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 5.

Session 18 Hours of lesson: 0 Instructor: M. Galeotti  Topics: The multiple linear regression model: relaxation of classical assumptions (autocorrelation, heteroskedasticity, stochastic regressors). Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 5. 
Session 19 Hours of lesson: 3 Instructor: M. Manera  Topics: The multiple linear regression model: relaxation of classical assumptions (autocorrelation, heteroskedasticity, stochastic regressors). Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 5. 
Session 20 Hours of lesson: 3 Instructor: M. Manera  Topics: The multiple linear regression model: relaxation of classical assumptions (autocorrelation, heteroskedasticity, stochastic regressors). Practical examples with the software EViews. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 5.
