Econometrics

Objectives

To acquire the theoretical knowledge and practical capabilities necessary for the use of the linear regression model with sectional and time series data.

General characterization

Code

1306

Credits

7,5

Responsible teacher

João Valle e Azevedo

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

Mandatory Precedence:

- 1303. Linear Algebra

- 1305. Statistics for Economics and Management

Bibliography

Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach, 5th Edition.

Resources

Moddle page containing class slides, econometric software tutorials and past exams. Availability of econometric software in the Computer Lab.

Teaching method

Theoretical Classes (approx. 72 hours), Practical Classes, including Computer Lab (approx. 36 hours)

Evaluation method

Final grade will be based on a group assignment (weighted 25%), midterm (weighted 25%) and on the final exam (weighted 50%). In order to pass, students must obtain a grade higher or equal to 8.0 in the final exam.

Regular Exam Period
Continuous assessment elements (and their weights):
Group assignment (weighted 25%), midterm (weighted 25%) and on the final exam (weighted 50%). In order to pass, students must obtain a grade higher or equal to 8.0 in the final exam.

Final exam (and their weighting): 50%



Resit Exam Period
Continuous assessment (and their weights) if different than 100%: NA


Final exam (and its weight): 100%
 




Grade Improvement in Regular Period
Continuous assessment (and their weights) if the scanning feature doesn’t count 100%: Group assignment (weighted 25%), midterm (weighted 25%) and final exam (weighted 50%). In order to pass, students must obtain a grade higher or equal to 8.0 in the final exam.
Final exam (and its weight):50% Grade Improvement in Resit Period
Continuous assessment (and their weights) if different than 100%:NA


Final exam (and their weighting): 100%


Subject matter

Multiple Linear Regression (Estimation and inference), Heteroskedasticity, Serial Correlation and Time Series Models.

Programs

Programs where the course is taught: