Econometrics

Code:

2M3E03

Acronym:

ECONM

Keywords
Classification	Keyword
OFICIAL	Economics

Instance: 2023/2024 - 1S

Active?	Yes
Course/CS Responsible:	Master in Economics of Business and Strategy

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MEEE	39	Syllabus	1	-	3	21	81

Teaching language

English
Obs.: Os materiais de apoio estão em língua inglesa

Objectives

Students should be able to master the main techniques for estimation of linear models, namely least squares, and instrumental variables.
Students should also learn econometric software in order to be able to put into practice what they learn.

Learning outcomes and competences

Upon conclusion of this unit the student should be able to correctly interpret empirical work and be able to identify how and under which circumstances he should use the estimation methods for linear models:

1. OLS (Ordinary Least Squares)


2. GLS (Generalized Least Squares)


3. IV (Instrumental variables)

On top of that the student should learn how to properly use econometric software for estimation of these models

Working method

Presencial

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

It is assumed that the student has been exposed to Introductory Econometrics at the undergraduate level.

Program

1. THE LINEAR REGRESSION MODEL

2. INTERPRETATION AND COMPARISON OF LINEAR REGRESSION MODELS

3. HETEROSKEDASTICITY AND AUTOCORRELATION

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

Mandatory literature

Marno Verbeek; A guide to modern econometrics. ISBN: 0-470-85773-0

Complementary Bibliography

William H. Greene; Econometric analysis. ISBN: 978-0-273-75356-8
Owen Jones; Introduction to scientific programming and simulation using R. ISBN: 978-1-4200-6872-6

Teaching methods and learning activities

Classes involves a mix of two complementary methodologies: expository classes and laboratorial work. Expository classes are used to present the models and estimation methodologies while "lab" sessions use a "hands-on" approach where students work directly with data to estimate and interpret the results.

Software

R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

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

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	45,00
Frequência das aulas	21,00
Trabalho escrito	15,00
Total:	81,00

Eligibility for exams

Students must attend at least 75% of classes

Calculation formula of final grade

Average of the two evaluations.
A minimum of 7.5 is required in each evaluation.