# GSF-6053 — Financial Econometrics I (Économétrie Financière) **Author / Auteur : Simon-Pierre Boucher** 📧 **Contact : [contact@spboucher.ai](mailto:contact@spboucher.ai)** 🏛️ Département de finance, assurance et immobilier — Faculté des sciences de l'administration, Université Laval [](https://www.latex-project.org/) [](https://www.stata.com/) [](#) [](#) [](#) --- ## 📚 Course Description This repository contains the complete teaching material for **GSF-6053 — Financial Econometrics I**, a graduate course (Hiver 2022) that introduces students to the practical aspects of econometric methods and estimation techniques as applied in finance. All lecture decks are written in **LaTeX (Beamer)**, in French, and each folder ships both the `.tex` source and the compiled `.pdf`. The course emphasizes modeling challenges specific to financial applications: OLS regression, maximum likelihood, hypothesis testing, heteroskedasticity, autocorrelation, panel data models, and time-series analysis (ARMA/ARIMA, unit roots, cointegration), with hands-on **Stata** application sessions. ### 📋 Prerequisites A basic understanding of statistics and linear algebra is recommended to engage fully with the course material. ## 🗂️ Detailed Content by Session ### Section 02 — Régression et Moindres Carrés Ordinaires (MCO / OLS) | Session | Folder | Topics | |---|---|---| | Séance 2 | `Séance 2/` | Modèle de régression, dérivation des MCO (sommation et format matriciel), exemples numériques, nuages de points et impact de la variance des erreurs | | Séance 3 | `Séance 3/` | Maximum de vraisemblance (MLE), MLE pour le modèle de régression linéaire, estimateur sans biais de la variance, propriétés des estimateurs MLE et MCO | | Séance 4 | `Séance 4/` | Analyse de variance, R², R² ajusté, tests d'hypothèses, contraintes linéaires, tests de Wald, LR (ratio de vraisemblance) et LM (multiplicateur de Lagrange), liens entre F, Wald, LR et LM | | Séance 5 | `Séance 5/` | Suite de la Section 02 (PDF compilé fourni) | ### Section 03 — Extensions au modèle linéaire simple | Session | Folder | Topics | |---|---|---| | Séance 6 | `Séance 6/` | Hétéroscédasticité : définition, conséquences et tests diagnostiques (Breusch-Pagan, White) | | Séance 7 | `Séance 7/` | Autocorrélation des erreurs : diagnostic (Durbin-Watson), transformations de Cochrane-Orcutt et Prais-Winsten, estimateur robuste de Newey-West | ### Section 04 — Les modèles panels | Session | Folder | Topics | |---|---|---| | Séance 8 | `Séance 8/` | Avantages des données panel, notation générale, modèle pooled, modèle à effets fixes, Least Squares Dummy Variable (LSDV) | | Séance 9 | `Séance 9/` | Modèle empilé, within-group estimator, modèle à effets aléatoires, transformation de Fuller-Battese | ### Section 05 — Les séries chronologiques | Session | Folder | Topics | |---|---|---| | Séance 10 | `Séance 10/` | Séries stationnaires et non stationnaires, opérateur de retard, autocovariance et autocorrélation (ACF/PACF), équations de Yule-Walker, modèle AR(1) | | Séance 11 | `Séance 11/` | Estimation des moments, tests de bruit blanc (Box-Pierce, Ljung-Box), tests de racine unitaire (Dickey-Fuller augmenté, Phillips-Perron), cointégration et tests de cointégration | | Séance 12 | `Séance 12/` | Processus autorégressifs (AR), moyennes mobiles (MA), ARMA(p,q), ARIMA(p,d,q), méthodologie Box-Jenkins | ### 💻 Stata Application Sessions | Session | Folder | Topics | |---|---|---| | STATA S02 | `STATA_S02/` | Application Stata de la Section 02 : statistiques descriptives, régressions MCO, t-test, F-test, analyse des résidus (avec captures d'écran des sorties Stata) | | STATA S03 | `STATA_S03/` | Application Stata de la Section 03 : détection et correction de l'hétéroscédasticité (tests de Breusch-Pagan et de White, tables du χ²) | ## 📁 Repository Structure ``` GSF6053/ ├── README.md ← this file ├── Séance 2/ … Séance 12/ ← lecture decks (Beamer .tex + compiled .pdf + build files) │ └── GSF6053_S.tex/.pdf ├── STATA_S02/ ← Stata lab, Section 02 (deck + Stata output screenshots .png) │ └── GSF6053_STATA_02.tex/.pdf └── STATA_S03/ ← Stata lab, Section 03 (deck + Stata output screenshots .png) └── GSF6053_STATA_03.tex/.pdf ``` Each session folder contains: - **`.tex`** — the Beamer source (UTF-8, French) - **`.pdf`** — the compiled slide deck, ready to use - **`.png` / `.jpg`** — figures, regression outputs, statistical tables referenced by the deck - Auxiliary LaTeX build files (`.aux`, `.log`, `.nav`, `.out`, `.snm`, `.toc`, `.synctex.gz`) ## 🔨 Building the Slides All decks compile with a standard TeX distribution (TeX Live / MacTeX). From a session folder: ```bash cd "Séance 2" pdflatex GSF6053_S2.tex pdflatex GSF6053_S2.tex # second pass for the table of contents / navigation ``` Or compile everything at once from the repository root: ```bash for d in "Séance "*/ STATA_S0*/; do (cd "$d" && f=$(ls *.tex 2>/dev/null) && [ -n "$f" ] && pdflatex -interaction=nonstopmode "$f" && pdflatex -interaction=nonstopmode "$f") done ``` **Requirements:** `beamer`, `graphicx`, `inputenc` (utf8) — all included in any full TeX Live / MacTeX install. Images must stay next to their `.tex` file (they are referenced by relative path). ## 🎓 Learning Outcomes By the end of this course, students will be able to: - Derive and apply the OLS and maximum-likelihood estimators to financial data; - Perform and interpret hypothesis tests (t, F, Wald, LR, LM); - Diagnose and correct heteroskedasticity and autocorrelation; - Estimate panel-data models (pooled, fixed effects, random effects); - Model financial time series (AR, MA, ARMA, ARIMA), test for unit roots and cointegration, and apply the Box-Jenkins methodology; - Implement all of the above in Stata. ## 🤝 Contributing This repository is primarily for educational purposes. If you find any errors or have suggestions for improvements, feel free to open an issue or submit a pull request. ## 📄 License & Usage This material is intended for students enrolled in the GSF-6053 course and for educational use. For any other use, please contact the author. --- **Simon-Pierre Boucher** — 📧 [contact@spboucher.ai](mailto:contact@spboucher.ai) ⭐ *Star this repository if you find it helpful!* ⭐