Econometrics: weeks 9-12 (April 8th to 28th)

In weeks 6-8 we:

• Finished working with the CEO compensation and Wooldridge's "Beauty" datasets
• Explained Lesson 4.1 Discrete and semi-continuous regressors. Polynomial terms
• Started Lesson 4.2 Collinearity
• Solved the Exercise sheet #2

The goals for weeks 9-12 are:

• Finishing Lesson 4.2 Collinearity
• Doing a partial exam (final date: Wednesday April 10th) including all the course content up to and including discrete regressors
• Explaining Lessons 5.1 and 5.2, Heteroscedastic and non-normal dat and Influential observations and outliers, as well as the corresponding practical cases

Econometrics: weeks 6-8 (March 11th to April 7th)

In weeks 3-5 we:

• Explained Lessons 3.1 (Estimation) and 3.2 (Inference)
• Reviewed the practical materials: Exercise sheet #1, Weight vs. age for a sample of children and CEO compensation dataset (Wooldridge's ceosal2 dataset)

The goals for weeks 6-8 are:

• Finish working with the CEO compensation dataset by fitting a log-linear model. We will see how this helps when modeling nonnormal data
• Explaining Lesson 4.1 Discrete and semi-continuous regressors. Polynomial terms
• Modeling Wooldridge's "Beauty" dataset
• Solving part of the Exercise sheet #2 and Many least squares calculations done (almost) by hand 
• Doing a partial exam (final date: April 10th)

Econometrics: weeks 3-5 (February 18th to March 10th)

In weeks 1-2 we:

• Explained Lessons 1-2
• Provided an introduction to Gretl, using mainly the dataset: Simulated data 

The goals for weeks 3-5 are:

• Explaining Lessons 3.1 (Estimation) and 3.2 (Inference)
• Reviewing the practical materials Many least squares calculations done (almost) by hand and Weight vs. age for a sample of children
• We will do some applied work with the CEO compensation dataset (Wooldridge's ceosal2 dataset)