Learning outcomes

To enable the student to master the techniques that will enable him/her to deal with empirical data. A particular effort is made to make the theoretical concepts understood through numerical illustrations and not formal demonstrations.

Content

1 Covariance and Regression - Introduction to Linear Regression

2 Inference - Introduction to Inference in Linear Regression

3 Multicollinearity - FWL Theorem and VIF

4 Regression Model Assumptions - Gauss-Markov Theorem, Functional Form, and Specification

5 Endogeneity - Instrumental Variables and 2SLS

6 Heteroscedasticity - Inference Correction and Weighted Least Squares (wls)

7 Serial Correlation - Inference Correction and Cochrane-Orcutt Method

8 Maximum Likelihood - LM, LR, and Wald Tests

9 Binary Dependent Variable - Logit and Probit

10 Censoring and Selection - Tobit and Heckman

11 Time Series - Stationarity and Cointegration

12 Panel Data - Fixed Effects and Random Effects


 
 

 

Assessment method

Written examination consisting of two parts. The first part is on theory and the second part is on exercises. For the exercise part of the exam, part of the mark comes from the work done during the year. This is a part of the examination and does not result in a separate mark. For the theory part, part of the mark comes from the continuous assessments made during the year. This is part of the examination and does not result in a separate mark. The relative weight of the different parts is as follows: 12/20 for the final exam, 5/20 for the practical work and 3/20 for the continuous assessments. During the examination, students are allowed one A4 sheet of paper, handwritten in ink (not crumpled), on which they write whatever they wish.

Sources, references and any support material

Reference book: Dougherty, Christopher, 2011. "Introduction to Econometrics," Oxford University Press, edition 4. The slides used in the course are lextensively based those used in this book.

Language of instruction

Français