32626 - Econometrics

Learning outcomes

The course provides an elementary but comprehensive introduction to the practice of econometrics, useful to correctly interpret estimates and develop simple empirical projects. By the end of the course the student will have adequate knowledge of linear regression models for the analysis of cross-sectional data (and a preliminary understanding of models for the analysis of panel data) and he/she will be able to understand under what conditions linear regression model estimates have a causal interpretation.

Course contents

IMPORTANT INFORMATION FOR INCOMING STUDENTS.The course requires prerequisite knowledge in descriptive statistics, probability and statistical inference (see the EPOS course Statistics and Programming as an example)

1. Introduction to the course: Economic questions and data

2. Review of probability and statistics

3. Linear regression model with one regressor

4. Linear regression model with multiple regressors

5. Nonlinear regression functions

6. Assessing studies based on multiple regression

7. Regression with panel data

8. Linear probability model

8. Instrumental variables regression

9. Experiments and quasi experiments

Readings/Bibliography

James H. Stock and Mark W. Watson (2020): Introduction to Econometrics, 4th Edition, Fourth Edition, Global Edition. Pearson

 

Additional material will be made available to enrolled students on the Virtuale Platform.

Teaching methods

Throughout the course the theoretical presentation of econometric methods is motivated by relevant socio-economic applications and each method is illustrated with applications using real data. For this reason, attendance to the lectures is warmly recommended.

Students will receive data and learn how to use the basic tools to perform their own empirical projects. To this aim, the course involves some lab sessions using the econometric software STATA, which is available to them through the University of Bologna CAMPUS license.

Assessment methods

The exam is closed book and assesses both the acquired knowledge of theoretical concepts and the ability to apply the methods learned and to interpret the estimation results in the light of the underlying theory.

The full exam lasts 1 hour and 30 and is divided into two components.

The first one is mainly theoretical and entails:

• 3 true or false questions with explanation
• 2 open questions
  

The second one is empirical and contains 5 interpretation questions whose answers are based on a STATA regression output (received from the instructor).

There will be one mid-term and one final exam lasting 1 hour, with the same structure. The theoretical component will include:

• 2 true or false questions with explanation
• 1 open question

The empirical component will include 4 interpretation questions whose answers are based on a STATA regression output (received from the instructor).

Students sitting the mid-term can take the final exam on the first examination date set for the full exam, right at the end of the course, or on the following call. A student can sit the final exam only once; if he/she fails or rejects the grade obtained, he/she will have to resit the full exam and will loose the grade obtained in the mid-term.

The maximum possible score is 30 cum laude, in case all anwers are correct, complete and formally rigorous.

The grade is graduated as follows:

<18 failed
18-23 sufficient
24-27 good
28-30 very good
30 e lode excellent

Students can reject the grade obtained at the exam once. To this end, he/she must email a request to the instructor within the date set for registration. The instructor will confirm reception of the request within the same date.

In case the student sit for the mid-term and the final, the course grade will be the average of the two exams. Rejection is intended with respect to the whole exam. If the grade is rejected, the student must retake the full exam (on the whole program). The only grade that can be rejected without any communication from the student is the one of the mid-term: when the student wants to reject the mid-term grade, she/he will simplu have to sit the full exam (tloosing the grade obtained in the first mid-term).

In case the student sit for the mid-term and the final, the course grade will be the average of the two exams. Rejection is intended with respect to the whole exam. If the grade is rejected, the student must retake the full exam (on the whole program). The only grade that can be rejected without any communication from the student is the one of the mid-term: when the student wants to reject the mid-term grade, she/he will simplu have to sit the full exam (tloosing the grade obtained in the first mid-term).

The use of AI is prohibited both for the written exam and for solving the evaluated empirical exercises. Any use constitutes a violation of academic integrity.

 

 

 

 

 

 

Teaching tools

Dedicated page on the VIRTUALE platform containing:

• News and updated information
• Lectures slides
  
• STATA lab material/examples of empirical applications

Software STATA: can be installed on students' personal computers (CAMPUS license) and is available at the Computer Labs of UNIBO.

AI use can be helpful for individual study and for self-assessing one’s preparation.

Office hours

See the website of Chiara Monfardini