Description

This unit of teaching comprises of two parts:

1. Advanced Econometric Theory III

This part covers advanced econometric methods that are not covered in the M1. It is a core method course of the M2 and significantly enhances the method portfolio of the students.

• Resampling Methods (Bootstrap, wild bootstrap, block bootstrap, bias corrections, subsampling)
• Quasi Maximum Likelihood
• Simulation Based Methods (simulated ML and Moment estimation, Indirect Inference)
• Bayesian Methods
• EM Algorithm
• Measurement Error and Missing Data Problems

1. Econometric Evaluation of Public Policies

This part covers important methods for treatment evaluation. It introduces the concept of counterfactual outcomes, treatment effects and self-selection and presents various approaches for the identification and estimation of the treatment effects:

• (Double-)Difference in Differences
• Regression Discontinuity (Sharp&Fuzzy)
• Propensity score weighting
• Inverse Probability weighting
• Matching methods
• IV methods (Local average treatment effects)
• Synthetic control groups

Compétences visées

On completing the course a student will be able to:

• Choose econometric model specifications which are suitable, both to the data and to the economic models;
• Understand estimation and inference methods which are appropriate for different econometric problems;
• Estimate the model and be able to interpret the estimation results, using appropriate software (part 2).
• Gather practical work experience with a statistical package as preparation of an empirical master dissertation (part 2).
• Conduct independent empirical work and present the results to an audience (part 2).

Disciplines

• Sciences économiques

Contacts

Responsable(s) de l'enseignement

• Bertrand Koebel