Code & Data
Teaching code, notebooks, and data
Find course supports, exercises, datasets, programs, notebooks and simulators, organized by course and session. The Homogeneity teaching note has its own Python and R replication code and practical instructions.
CY Cergy Paris Université · 2026/2027
Econometrics — L3
Chapter-based exercises, teaching datasets, and previous continuous assessments with supplied answer keys.
Chapter exercises
Open resource
Chapter II — Simple regression applications
Chapter III — Probability and random variables
Chapter IV — Inference in simple regression
Chapter V — Estimator properties
Chapter VI — Multiple regression applications
Chapter VII — Hypothesis tests and specification
Chapter VIII — Functional form and model comparison
Teaching datasets
Teaching dataset collection — original formats and dictionaries
BWGHT — bwght.csv
BWGHT — BWGHT.DES
BWGHT — BWGHT.raw
BWGHT — bwght.sas7bdat
BWGHT — bwght.xls
CAMPUS — campus.des
CAMPUS — campus.raw
CAMPUS — campus.xls
CEOSAL1 — CEOSAL1.DES
CEOSAL1 — CEOSAL1.raw
CEOSAL1 — ceosal1.xls
GPA1 — GPA1.DES
GPA1 — GPA1.raw
GPA1 — gpa1.xls
GPA2 — gpa2.csv
GPA2 — GPA2.DES
GPA2 — gpa2.raw
GPA2 — gpa2.sas7bdat
GPA2 — gpa2.xls
HPRICE1 — hprice1.csv
HPRICE1 — HPRICE1.DES
HPRICE1 — hprice1.raw
HPRICE1 — hprice1.sas7bdat
HPRICE1 — hprice1.xls
HPRICE2 — hprice2.csv
HPRICE2 — HPRICE2.DES
HPRICE2 — HPRICE2.raw
HPRICE2 — hprice2.sas7bdat
HPRICE2 — hprice2.xls
HTV — htv.csv
HTV — htv.des
HTV — htv.raw
HTV — htv.sas7bdat
HTV — htv.xls
JTRAIN — JTRAIN.DES
JTRAIN — JTRAIN.raw
JTRAIN — jtrain.xls
MEAP93 — meap01.des
MEAP93 — meap01.raw
MEAP93 — meap01.xls
MEAP93 — MEAP93.DES
MEAP93 — MEAP93.raw
MEAP93 — meap93.xls
NBASAL — nbasal.csv
NBASAL — nbasal.des
NBASAL — nbasal.raw
NBASAL — nbasal.sas7bdat
NBASAL — nbasal.xls
RDCHEM — rdchem.csv
RDCHEM — RDCHEM.DES
RDCHEM — RDCHEM.raw
RDCHEM — rdchem.sas7bdat
RDCHEM — rdchem.xls
RENTAL — rental.csv
RENTAL — RENTAL.DES
RENTAL — RENTAL.raw
RENTAL — rental.sas7bdat
RENTAL — rental.xls
TWOYEAR — twoyear.des
TWOYEAR — twoyear.raw
TWOYEAR — twoyear.xls
WAGE1 — WAGE1.DES
WAGE1 — WAGE1.raw
WAGE1 — wage1.xls
Previous continuous assessments (CC)
CC 2017-18 — Question paper
CC 2018-19 — Question paper
CC 2019-20 — Question paper
CC 2021-22 — Question paper
CC 2022-23 — Question paper
CC 2022-23 — Answer key
CC 2023-24 — Question paper
CC 2024-25 — Question paper
CC 2024-25 — Answer key
CY Cergy Paris Université · 2026/2027
Econometrics — M1
An exercise collection covering regression foundations, matrix formulation, inference, heteroskedasticity, nonlinear least squares, and instrumental variables.
Course materials
Econometrics exercises
CY Cergy Paris Université · 2026/2027
Econometrics — M2 & PhD
An advanced exercise collection on linear models, inference, instrumental variables, maximum likelihood, and generalized method of moments.
Course materials
Advanced econometrics exercises
Sciences Po Saint-Germain-en-Laye · 2026/2027
Microeconomics — L1
Session 1 introduces consumers, producers, price elasticity, market efficiency, transfers, and deadweight loss.
Course materials
Session 1 — Consumers, producers, and market efficiency
Session 1 — Accompanying readings
Université Catholique de l’Ouest · 2026/2027
Microeconomics III — L3
Microeconomic foundations, strategic decisions and Nash equilibrium, followed by risk, insurance, portfolio choice and private information, with exercises, Python/R programs and interactive simulators.
Part I — Foundations
Séance 1 — Introduction générale
Part II — Strategic decisions and games
Séances II à IV — Théorie des jeux : décisions stratégiques et équilibre de Nash
Simulateur — Jeux répétés
Simulateur — Pile ou face
Part III — Risk and information
Annex-generation utility
Course figures and calculations — Python
Course figures and calculations — R
Information cascade simulator
Portfolio choice simulator
Diversification and correlation simulator
Risk pooling simulator
Part III — Course support (sessions V–VIII)
Exercise series 5
Exercise series 6
Exercise series 7
Exercise series 8
Supplement — Pindyck & Rubinfeld
Supplement — Varian
Complete folder — supports, code, data and supporting checks
Practical README
Université Catholique de l’Ouest · 2026/2027
Econometrics — L3
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.
Practical instructions
Practical README
Session 1 — Covariance, correlation and regression
Annex-generation utility — py
Data export utility — py
Course — do
Course — py
Course — R
Course — Python notebook
Course — R notebook
Exercises — do
Exercises — py
Exercises — R
Exercises — Python notebook
Exercises — R notebook
Dataset — attend
Dataset — cps78_85
Dataset — econmath
Dataset — jtrain2
Dataset — phillips
Dataset — wagepan
Dataset — wine
Interactive covariance simulator
Course support
Exercise sheet
Complete folder — supports, code, data and supporting checks
Session 2 — Sampling, probability and inference
Annex-generation utility — py
Data export utility — py
Course — do
Course — py
Course — R
Course — Python notebook
Course — R notebook
Exercises — do
Exercises — py
Exercises — R
Exercises — Python notebook
Exercises — R notebook
Dataset — attend
Dataset — cps78_85
Dataset — econmath
Dataset — htv
Dataset — jtrain2
Dataset — nbasal
Dataset — phillips
Dataset — wagepan
Dataset — wine
Interactive sampling simulator
Course support
Exercise sheet
Complete folder — supports, code, data and supporting checks
CY Cergy Paris Université · 2026/2027
Data Analysis with Python — L3
Five applied sessions on price indices, unemployment, data cleaning, dataset merges, and income distributions, with source data and reproducible Python notebooks.
TD01
TD01 — Price indices: instructions
TD01 — Price indices: notebook
Complete folder — supports, code, data and supporting checks
TD02
TD02 — Unemployment: instructions
TD02 — Unemployment: notebook
Complete folder — supports, code, data and supporting checks
TD03
TD03 — Wage data: instructions
TD03 — Wage data: notebook
TD03 — Wage data: CSV
TD03 — Wage data: dictionary
Complete folder — supports, code, data and supporting checks
TD04
TD04 — Dataset merges: instructions
TD04 — Dataset merges: notebook
TD04 — Presentation slides
Additional course notes
Complete folder — supports, code, data and supporting checks
TD05
TD05 — Income distributions: notebook
TD05 — Income distributions: instructions
Session instructions — HTML
Complete folder — supports, code, data and supporting checks
Teaching Note · October 2021
Homogeneity — Teaching Note replication code
Python and R programs derive the restrictions, generate the figures, run the simulations and compare the estimates. The supplied simulation data and output tables accompany the code.