Econometrics

Code:

2ME03

Acronym:

E

Keywords
Classification	Keyword
OFICIAL	Economics

Instance: 2022/2023 - 1S

Active?	Yes
Course/CS Responsible:	Master in Economics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
ME	70	Official Syllabus after 2021-2022	1	-	6	42	162

Teaching language

Portuguese and english
Obs.: Os materiais de apoio estão em língua inglesa.

Objectives

The course of Econometrics is aimed for students of the Master's degree in Economics. It requires basic knowledge in Econometrics, acquired in the course of the 1st cycle degree programme in Economics or an equivalent course, and also, demands a good understanding of Linear Algebra and Statistics. The course objective is to broaden the preparation of students in econometric methods, introducing some of the methods most frequently used in recent literature. In the course we will apply these methods showing how to use econometric packages in empirical research. The syllabus of the course is strongly supported by the recommended book that offers a modern approach to Econometrics, always making explicit the assumptions underlying the different models and using multiple empirical examples.

It is intended that the student acquires a suitable background that allows him to develop his research work in graduate school or at least understand the skills he needs to complete his empirical work. However, it should be noted that, due to lack of time and by the professor's choice, the syllabus does not cover topics in ​​Macroeconometrics/Time Series Analysis. Nevertheless, this course gives the foundations for a study of these and other topics in courses of quantitative methods that are part of the Master's Course.

Learning outcomes and competences

Students are expected to get acquainted with several methods of estimation in Econometrics (OLS, FGLS, ML, IV, GMM, etc.), and recognize situations that justify their use. They must also learn to use econometric software for data manipulation and model estimation.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

The course requires solid knowledge in Econometrics, at least at the level of 6 ECTS in the 1st cycle, that is, at the introductory level. Besides estimation and statistical inference, it requires a reasonable background in Statistics and Mathematics. Other pre-requisites include knowledge of Calculus and, in particular, Linear Algebra.

Program

1. A REVIEW OF LINEAR REGRESSION

2. INTERPRETING AND COMPARING REGRESSION MODELS

3. HETEROSKEDASTICITY AND AUTOCORRELATION

4. ENDOGENOUS REGRESSORS, INSTRUMENTAL VARIABLES AND GENERALIZED METHOD OF MOMENTS

5. MODELS BASED ON PANEL DATA

6. MAXIMUM LIKELIHOOD ESTIMATION AND SPECIFICATION TESTS

7. MODELS WITH LIMITED DEPENDENT VARIABLES

Mandatory literature

Marno Verbeek; A guide to modern econometrics. ISBN: 978-1-119-47211-7

Complementary Bibliography

Christopher Baum; An Introduction to Modern Econometrics Using Stata, Stata Press
William H. Greene; Econometric analysis. ISBN: 978-0-273-75356-8

Teaching methods and learning activities

In class, basic theory will be presented, computer usage of methods and models will be documented and practiced and main results discussed.

Software

Stata

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	50,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Frequência das aulas	42,00
Estudo autónomo	58,00
Apresentação/discussão de um trabalho científico	4,00
Elaboração de projeto	10,00
Trabalho de investigação	24,00
Trabalho escrito	24,00
Total:	162,00

Eligibility for exams

Regular attendance is expected.

Calculation formula of final grade

Grades will be assigned according to the general rules of the University of Porto. A final written exam will be held at the end of the term. 

Alternatively, the student may choose to do a project in the course of the semester and an exam that covers all the materials. The final grade is the average of both scores. A positive grade requires a average of 10 (out of 20) and no partial score below 7,0 (out of 20).


The final grade will be assigned in the 0/20 points scale. A grade lower than 10 is taken to mean a failure. 

Examinations or Special Assignments

Students that opt for the project will have to deliver a proposal, the final project, and present the work in class. Failure to do so means the student is automatically qualified for an evaluation exclusively by final exam.

Classification improvement

Only by final exam.