Advanced Econometrics_2021

Welcome Message,

Msc Student in Economics!

 Academic year 2021 

Hello everyone,

This is a team of Prof. Boysen-Hogrefe Jens (PhD) and Aimable Nsabimana (PhD) who welcome you in this Module. This is Advanced Econometrics, a Module of 15 credits designed to be offered to Master students in Economics at the University of Rwanda. This Module is mainly focusing on cross section and panel data methods and it recognizes that students have already covered the basic linear model in their undergraduate studies. The Methods of advanced cross sections and panel data methods are becoming increasingly popular, especially in examining the causal impact associations. This is a an advanced course that requires students to read regularly class materials and guided book chapters so that they can effectively understand the module. The module is approximated to last two months periods within which the students will cover eight chapters as will be detailed in subsequent sections.

The module focuses on the role played by the assumptions with economic content while downplaying or ignoring regularity conditions.

 Module Aim

The module aims to equip students with very firm understanding of why certain methods work while other not. It will also give students the background for developing new methods.

Module Features 

  I. Asymptotics

II. Ordinary least squares (OLS) and Inference under heteroscedasticity

III. Generalized least squares (GLS), Feasible generalized least squares (FGLS) and  Seemingly unrelated regressions (SUR)

IV. Maximum Likelihood (ML) and inference

V. Generalized method of moments (GMM)

VI. Endogeneity and instrumental variables (IV)

VII. Two Stage Least Squares and Three Stage Least Squares (SEM)

VIII. Binary Choice models (Logit/Probit)

Module Overall Learning Outcomes 

At the end of this module, students will be able to:

(i) Get familiar with the use of analogy approach complemented by asymptotic analysis in estimating OLS Models

(ii) Understand and be able to apply various assumptions of the underlying population models.

(iii) Get familiar with the assumptions, couched in terms of correlations, conditional expectations, conditional variances and covariances and conditional distributions

(iv) Understand source and implications of endogeneity problem in deriving causal impact and how to deal with it
(v) Estimating binary response models (LPM, Logit and Probit)

 Module Facilitators 

 Module Leader: Prof. Boysen-Hogrefe, Jens (PhD)

Module Partner: Dr. Aimable Nsabimana (PhD)

Tel: +250788766939
Email: a.nsabimana17@ur.ac.rw  

 General Module Resources 

(i) Greene, W. (2018). Econometrics Analysis, Pearson.
(ii) Wooldridge, Jeffrey M. (2010). Econometric Analysis of Cross Section and Panel Data, MIT Press.