Econometrics II

Objectives

1 Consolidate the knowledge acquired in the curricular unit of Econometrics I;
2 Understand the Linear Probability Model limitations and the reasons why it shouldn’t be used;
3 Understand the scope and the conditions for implementation of logistic regression models;
4 Develop and interpret, statistically and economically, logistic regression models;
5 Understand the scope and the conditions for implementation of time series regression models;
6 Develop and interpret estimated time series regression models;
7 Understand the scope and the conditions for implementation of panel data regression models;
8 Develop and interpret estimated panel data regression models.

General characterization

Code

100050

Credits

6.0

Responsible teacher

Manuel José Vilares

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

Recommended: Descriptive and Inferential Statistics. Linear Algebra. Econometrics I.

Bibliography

Wooldridge, Jeffrey M. (2008). Introductory econometrics: a modern approach, 4th ed. South-Western. ISBN 9780324585483; Griffiths, W. E., Hill, R. C. e Judge, G. G. (1993). Learning and Practicing of Econometrics, John Wiley and Sons. ISBN 0471513644; Ajmani, V. (2009). Applied Econometrics Using the SAS System. John Wiley & Sons. ISBN 9780470129494; Johnston, J.; Dinardo, J. (1997). Econometrics Methods. 4th Edition, Economics Series, McGraw Hill (Existe também tradução em português). ISBN 007115342X; Vilares, M.J.; Coelho, P.S. (2011). Satisfação e Lealdade do Cliente – Metodologias de Avaliação, Gestão e Análise. 2ª Edição, Escolar Editora. ISBN 9789725923160

Teaching method

1 Linear regression models review;
2 Regression models for qualitative dependent models;
3 Time series models;
4 Panel data models.

Evaluation method

In the first round of exams, students can choose 1 from 2 options of grading:
a) Term project (40%) + Participation and midterm (20%) + Final exam (40%)
b) Participation and mid-term tests (30%) + Final exam (70%)
The final grade obtained in the first round of exams matches the best of the two grades obtained in the different grading options.

In the second round of exams, the final grade is the best from: Final exam (100%) or Participation and midterm (30%) + Final exam (70%).
However, to get approval (an average above 9,5 points), students must not have a grade lower than 9,5 points (in a scale from 0 to 20) in the final exam.

Subject matter

1 Linear regression models review;
2 Regression models for qualitative dependent models;
3 Time series models;
4 Panel data models.

Programs

Programs where the course is taught:

Bachelor Degree in Information Management