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A86050 Quantitative Methods for Economics, Finance and Management

Scuola di Economia e Management
Syllabus
Academic Year 2014/15 First Semester

Docente Titolare	Matteo Manera
E-mail	mmanera@liuc.it
Office	"Torre" (main tower), 4th floor
Phone

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 real-world empirical economic and financial problems. It covers most of the tools used in modern econometrics research, e.g. correlation, regression and extensions for time-series methods. During the course extensive use of real data examples is made and students are involved in hands-on computer work.

Learning targets

At the end of the course students will be able to approach relatively complex economic and financial problems involving both time-series and cross-sectional 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 non-technical 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 non-tecnical introduction to regression. Practical examples with the software E-Views. 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 non-technical 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 t-test. Practical examples with the software E-Views. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 2-3.
Session 5 Hours of lesson: 3 Instructor: M. Galeotti	Topics: The simple linear regression model under classical assumptions: OLS and t-test. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 2-3.
Session 6 Hours of lesson: 3 Instructor: M. Manera	Topics: The simple linear regression model under classical assumptions: OLS and t-test. Practical examples with the software E-Views. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 2-3.
Session 7 Hours of lesson: 3 Instructor: M. Galeotti	Topics: The simple linear regression model under classical assumptions: OLS and t-test. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 2-3.
Session 8 Hours of lesson: 3 Instructor: M. Manera	Topics: The simple linear regression model under classical assumptions: OLS and t-test. Practical examples with the software E-Views. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 2-3.
Session 9 Hours of lesson: 3 Instructor: M. Galeotti	Topics: The simple linear regression model under classical assumptions: OLS and t-test. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapters 2-3.
Session 10 Hours of lesson: 3 Instructor: M. Galeotti	Topics: The multiple linear regression model under classical assumptions: OLS, F-test, 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, F-test, dummy variables, forecasting. Practical examples with the software E-Views. 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, F-test, 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, F-test, dummy variables, forecasting. Practical examples with the software E-Views. 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, F-test, 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, F-test, dummy variables, forecasting. Practical examples with the software E-Views. 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 E-Views. 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 E-Views. 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 E-Views. Readings: Koop, G. (2007), Introduction to Econometrics, New York: John Wiley and Sons, Chapter 5.

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