Econometrics II
Objectives
- Formulate and specify econometric models to interpret economic phenomena for sectional data, time series and panel data;
- Recognize and understand completely the multiple linear regression model used in time series and panel data; Introduction to causal inference methods.
General characterization
Code
100050
Credits
6.0
Responsible teacher
Bruno Miguel Pinto Damásio
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
Teaching method
1 Linear regression models review;
2 Regression models for qualitative dependent models;
3 Time series models;
4 Panel data models.
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.
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.
2 Regression models for qualitative dependent models;
3 Time series models;
4 Panel data models.