28885 - Econometrics 1

Course Unit Page

  • Teacher Matteo Barigozzi

  • Credits 6

  • SSD SECS-P/05

  • Teaching Mode Traditional lectures

  • Language English

  • Campus of Bologna

  • Degree Programme Second cycle degree programme (LM) in Economics (cod. 8408)

  • Course Timetable from Sep 21, 2021 to Oct 28, 2021

SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

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, available on line, 2019
  • Jeff M. Wooldridge: Introductory Econometrics. A Modern Approach, 6th Edition, South-Western, 2018

OTHER REFERENCES

  • William H. Greene, Econometric Analysis, 8th Edition, Pearson, 2018
  • Chris Baum, An Introduction to Modern Econometrics Using Stata, Stata Press

Teaching methods

Throughout the course, the presentation of theoretical issues will be complemented by critical discussion of some micro-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.

During the course Computer Exercises will be assigned to small groups of students and will be due on specific dates according to the rules communicated on Virtuale. The average mark of the Computer Exercises will account for the 40% of the final mark. 

The written exam is closed book. 

The maximum possible score is 30 cum laude, in case all anwers 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

For students attending the Master in Economics LMEC the Econometrics 1 grades will contribute to the final grade of the integrated course Quantitative Methods For Economic Analysis (I.C). The latter is obtained as the average of the grades of Econometrics 1 and Mathematical Economics (see the LMEC booklet on the LMEC webpage for detailed information on the integrated courses' exams).

 

Teaching tools

Slides and digital notes handwritten in class.

Software STATA, available at the Computer Lab of the School of Economics and Management.

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

Office hours

See the website of Matteo Barigozzi