International opportunities for students
Course: Econometrics


Instructor: Carlo Fezzi, carlo.fezzi [at]  
Credits: 8

Course objectives

The ultimate objective of this course is to provide students with the confidence and the expertise to apply econometric methods to the analysis of data in a broad range of settings. The course concentrates on the practical use of econometric methods. The relevant methodologies are discussed in class and then implemented during lab sessions, where students have the opportunity to gain hands-on experience using the free software R.
At the end of the course, students will be able to: 
a) Recognize the appropriate approach for the estimation of different econometric models with cross-sectional, panel and time series data;
b) Understand the main assumption underpinning different types of econometric models and estimators;
c) Develop econometric analyses using empirical data in order to answer specific economic questions;
d) Critically evaluate the econometric analyses carried out by others.


The topics covered in this course include:

  • econometrics vs statistics: what is the main difference between these two disciplines? 
  • review of liner regression and statistical inference
  • heteroscedasticity and autocorrelation
  • endogeneity and instrumental variables
  • simultaneous equations models 
  • panel data analysis
  • maximum likelihood estimators 
  • discrete choice models 
  • time series analysis of stationary and non-stationary processes


This course requires prior knowledge of linear algebra (e.g. matrix multiplication), statistical methods (e.g. probability distributions, statistical tests) and introductory econometrics methods (e.g. linear regression). This material is covered in the first half of the course: "advanced data analysis and mathematical models, 121120". Prior knowledge of the free software R will be an advantage, but it is not necessary.

Teaching methods

The course alternates theoretical classes and hands-on sessions in the lab. During the theory classes, the students will be introduced to different econometric methods, their underpinning assumptions and their correct application. Each lab session will focus on the topics illustrated in the previous theory class, allowing the students to directly implement econometric methods to the analysis of empirical data.

Verification of learning

Students will be evaluated using a combination of three criteria: 1) a mid-term test, based on the material covered in the first 4 weeks of the course, worth 20% of the final grade, 2) an empirical project, to be carried out individually or as part of a group, worth 35% of the final grade, and 3) a final exam on the remaining part of the course, worth 45% of the grade. Those students who cannot attend the mid-term exam can still pass the course by submitting a personal empirical project (35% of the final grade) and take an exam on the entire program of the course (65%).


J. Johnston, J. DiNardo. Econometric Methods. 4th Edition. The McGraw-Hill. 1997.
W.H. Greene. Econometric Analysis. 7th Edition. Prentice Hall. 2011.
J.M. Wooldridge. Introductory Econometrics: a modern approach, 5th Edition. South-Western College Pub. 2012.
Train K. Discrete Choice Methods with Simulation, Cambridge University Press. 2009.
Hendry D., Juselius K. Explaining Cointegration Analysis. Part 1, The Energy Journal, 21:1, 1-42.
Hendry D., Juselius K. Explaining Cointegration Analysis. Part 2, The Energy Journal, 22:1, 1-52.