CY Cergy Paris Université · Tutorial
Econometrics — M2 & PhD
An advanced exercise collection on linear models, inference, instrumental variables, maximum likelihood, and generalized method of moments.
Teaching
I began teaching economics at CY Cergy Paris Université in 2019. I have taught lectures and tutorials in a range of subjects, including microeconomics, macroeconomics, data analysis, and econometrics, from undergraduate to doctoral level. This page presents selected materials for my courses in the 2026/2027 academic year. My teaching connects economic intuition, formal reasoning, and empirical evidence, with an emphasis on explaining why a method works and interpreting results carefully.
Courses & materials
Browse selected courses and their available exercises, notebooks, data, and presentation slides.
CY Cergy Paris Université · Tutorial
An advanced exercise collection on linear models, inference, instrumental variables, maximum likelihood, and generalized method of moments.
CY Cergy Paris Université · Tutorial
An exercise collection covering regression foundations, matrix formulation, inference, heteroskedasticity, nonlinear least squares, and instrumental variables.
CY Cergy Paris Université · Tutorial
Chapter-based exercises, teaching datasets, and previous continuous assessments with supplied answer keys.
CY Cergy Paris Université · Tutorial
Five applied sessions on price indices, unemployment, data cleaning, dataset merges, and income distributions, with source data and reproducible Python notebooks.
Sciences Po Saint-Germain-en-Laye · Lecture
Session 1 introduces consumers, producers, price elasticity, market efficiency, transfers, and deadweight loss.
Université Catholique de l’Ouest · Lecture & Tutorial
Session 1 introduces covariance, correlation, descriptive evidence, and simple regression. Session 2 develops sampling, probability, estimator distributions and inference, with course and exercise code in Python, R and Stata, notebooks, data and interactive simulators.
Université Catholique de l’Ouest · Lecture & Tutorial
Microeconomic foundations, strategic decisions and Nash equilibrium, followed by risk, insurance, portfolio choice and private information, with exercises, Python/R programs and interactive simulators.
Teaching approach
Begin with one durable objective and use short checks to make understanding visible.
Students should be able to state the assumptions, intuition, and consequences of failure.
Separate description, identification, and policy judgment in every empirical exercise.
In the room
Explanation, questions, and quantitative practice belong in the same conversation.
Student training in Stata, 2021.

Code & notebooks
The separate Code & Data page gathers the real Python, R, Stata, notebook, dataset, and simulator files with practical instructions.
Explore Code & DataTeaching enquiries