Econometrics

Code:

1EC303

Acronym:

ECONM

Keywords
Classification	Keyword
OFICIAL	Economics

Instance: 2018/2019 - 1S

Active?	Yes
Responsible unit:	Agrupamento Científico de Economia
Course/CS Responsible:	Bachelor in Economics

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
LECO	241	Bologna Syllabus since 2012	3	-	6	63	162

Teaching language

Portuguese

Objectives

The course is designed for a one-semester introduction to Econometrics. Pre-requisites for the course are a solid background in Economic Theory (both Micro- and Macroeconomics), in Statistics and in Linear Algebra. The course's main objective is in interpreting, understanding and evaluating the findings of elementary econometric analyses.

Learning outcomes and competences

The student is expected to be able to read, interpret and evaluate elementary econometric findings. Hands-on experience with specialized  econometric software is required.

Working method

Presencial

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

Pre-requisites for the course are a solid background in Economic Theory (both Micro- and Macroeconomics), in Statistics and in Linear Algebra.

Program

INTRODUCTION: SUBJECT AND METHODOLOGY OF ECONOMETRICS

1. THE CLASSICAL LINEAR REGRESSION MODEL
1.1. Elementary concepts and notation.
1.2. Least squares (OLS) estimators of the regression coefficients.
1.3. Coefficient of determination.
1.4. Assumptions of the classical linear regression model.
1.5. Properties of the OLS estimators.
1.6. The estimator of the variance of the disturbances.

2. INFERENCE IN THE LINEAR REGRESSION MODEL
2.1. The normality assumption.
2.2. Testing hypothesis about a single coefficient.
2.3. Testing hypothesis about linear restrictions on coefficients.
2.4. Testing the overall significance of the regression.

3. SOME EXTENSIONS OF THE LINEAR REGRESSION MODEL
3.1. Choosing a functional form.
3.2. Dummy variables.
3.3. Models for the trend and seasonal components.
3.4. Testing the equality between sets of coefficients in two regressions.

4. THE GENERALIZED LINEAR REGRESSION MODEL
4.1. Assumptions.
4.2. GLS and EGLS estimators.

5. HETEROSKEDASTICITY
5.1. The nature of the problem.
5.2. Detecting heteroskedasticity: White and Breusch-Pagan tests.
5.3. Estimation methods.

6. AUTOCORRELATION
6.1. The nature of the problem.
6.2. Stochastic process. The 1st-order autoregressive process.
6.3. Detecting autocorrelation: Durbin-Watson and Breusch-Godfrey tests.
6.4. Estimation methods.

7. REGRESSION MODELS FOR CATEGORICAL DEPENDENT VARIABLES
7.1. Introduction. Binary choice models.
7.2. The linear probability model.
7.3. The logit and probit models.

Mandatory literature

M. Mendes de Oliveira, Luis Delfim Santos e Natércia Fortuna; Econometria, 2ª ed., Escolar Editora, 2018

Complementary Bibliography

Gujarati, D.N. and Porter, D. C.; Basic Econometrics, 5th ed. , McGraw-Hill , 2009
Gujarati, D.; Econometrics by Example 2nd ed. , Palgrave, 2014
Griffiths, W.E., Hill, R. C. and Lim , G.C.; Using EViews for Principles of Econometrics 4th ed., John Wiley & Sons, 2012
Hill, R.C., Griffiths, W.E. and Lim, G.C.; Principles of econometrics 4th ed., John Wiley & Sons, 2012
Asteriou, D. and Hall S.G.; Applied Econometrics 3rd ed. , Palgrave, 2015
Heij, C., De Boer, P., Franses, P. H., Kloek, T. and Van Dijk, H. K.; Econometric Methods with Applications in Business and Economics, Oxford Oxford University Press, 2004

Teaching methods and learning activities

Econometrics aims to provide the basic theoretical principles of estimation and inference in Econometrics. Classes will be held in computer labs, allowing students' access to hands-on experience with computer applications using specialized econometric software.

A page in the computer system will provide valuable information on the plan and syllabus of the course, including formula sheets, statistical tables and a sample of tests and exams of previous years.

Portuguese-reading students can follow the textbook mentioned in the references. There are many fine textbooks in English available for students unable to read in Portuguese; please refer to your lecturer for details and advice.

Software

Eviews

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation	Weight (%)
Teste	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Frequência das aulas	56,00
Total:	56,00

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 submit to two tests in the course of the semester. The student's ability to use a computer for estimation and testing will be tested in the first one. The second test is a comprehensive written exam following the end of regular classes. The final grade is the weighted average of both scores, with weights of 40% to the first and 60% to the second. A positive grade requires a weighted average of 10 (out of 20) and no partial score below 7,0.

In tests and exams, students should provide a valid ID and bring their own writing materials; in final exams, standard (non graphical) calculators are allowed. A formula sheet and standard statistical tables will be made available to students taking the final exams. All other materials are excluded.

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

Erasmus students are required to submit to tests and/or exams. No special work or project will substitute for the regular method of assessment.

Classification improvement

Only by final exam.