B2153 - Econometric Methods

Learning outcomes

At the end of the course the student has acquired knowledge of the basic instruments used by economists for their empirical investigations: the linear regression model and the Ordinary Least Squares method. In particular, he/she is able: - to critically understand the applications of this model in the recent empirical economic literature; - to apply the model and perform his/her own analysis of economic datasets using the software STATA.

Course contents

1. Introduction to the course. Conditional expectations and their features

2. Multiple linear regression analysis: Ordinary Least Squares (OLS) estimator

3. Finite Sample properties of OLS estimator

4. Finite Sample inference

5. OLS asymptotics and large sample inference

6. Specification tests and model selection

7. Non spherical variance

8. Incorporating non-linearities in multiple linear regression models

Readings/Bibliography

MAIN TEXTBOOKS

• Bruce Hansen, Econometrics, Princeton University Press, 2022

• Jeff M. Wooldridge: Introductory Econometrics. A Modern Approach, Cengage International Edition, 8th edition, 2025.

• Jeff M. Wooldridge: Econometric Analysis of Cross Sections and Panel Data, The MIT Press, 2nd Edition, 2010

OTHER REFERENCES

• Chris Baum, An Introduction to Modern Econometrics Using Stata, Stata Press

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

 

Teaching methods

Throughout the course, the presentation of theoretical issues will be complemented by critical discussion of some economic applications from recent research using linear regression models. Students will receive data to practice at the computer and learn the basic skills to perform empirical work using the software STATA.

Assessment methods

There are two components of the course assessment: take home assignments and written exam. Take home exercises are computer based, and they test the ability to apply the methods learnt in the classroom and by individual study to simulated or real data.

The written exam aims at testing the comprehension of theoretical concepts and the ability to interpret estimation results in the light of the underlying theory. It is divided in three parts:

• True or False (answer with concise motivation): 3 questions, 12 points
• Open question (formal answer to theoretical question): 1 or 2 questions, 8 points
• Interpretation question (answer on STATA log file with estimation output): 2 or 3 questions, 10 points

During the course two computer based take home assignments will be given to small groups of students and will be due on specific dates according to the rules communicated on Virtuale. The average mark of these take home assignments will account for the 30% of the final grade. The take home grade is valid for one year, i.e., until September of the year after it was taken.

The maximum possible score is 30 e lode, in case all answers are correct, complete and formally rigorous.

The exam is graded as follows:

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

The final grade can be rejected only once.

The written exam is closed book.

The use of AI is prohibited for the written exam. Any use constitutes a violation of academic integrity.

The take home assignments involve a substantial use of AI, such as problem-solving and content generation, and a mandatory critical analysis component.

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.

STATA Introductory course, offered before the beginning of the course by the teaching tutor in charge.

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

Office hours

See the website of Chiara Monfardini