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
1314
Credits
7
Responsible teacher
João Valle e Azevedo
Hours
Weekly - Available soon
Total - Available soon
Teaching language
English
Prerequisites
Mandatory precedence: Linear Algebra and Statistics for Economics and Management
Bibliography
Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach, 5th Edition.
Teaching method
Theoretical Classes presenting tools and methods to analyze empirically econometric models; Practical Classes where exercises are solved to consolidate ideas, but also including computer Lab sessions to get familiar with Econometric software; Course Assessment includes a group assignment: students need to pose a question, discuss literature around it, gather the data necessary to answer that question and formulate and interpret an econometric model.
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.