A structured collection of lecture notes and reproducible code covering the three core branches of applied econometrics: Cross Section, Time Series, and Panel Data.
Each module pairs the theory (identification, assumptions, estimators) with hands-on implementations in R and Python, so the same concept can be read, derived, and run end to end.
econometrics/
├── README.md
├── cross-section/
│ ├── aula1.R
│ ├── aula2.R
│ └── aula3.R
├── time-series/
│ ├── aula1.R
│ ├── aula2.R
│ └── aula3.R
└── panel-data/
├── aula1.R
├── aula2.R
└── aula3.R
Microeconometric methods applied to data observed at a single point in time.
Project: link here
Topics covered:
- Ordinary Least Squares (OLS) and the Gauss-Markov assumptions
- Heteroskedasticity, robust standard errors, and clustering
- Instrumental variables (IV) and Two-Stage Least Squares (2SLS)
- Binary and limited dependent variables (Logit, Probit, Tobit)
- Causal inference: matching, propensity scores, and selection
Modeling and forecasting data observed sequentially over time.
Project: link here ✅
Topics covered:
- Stationarity, unit roots, and the ADF and KPSS tests
- Autocorrelation, ACF, and PACF
- ARIMA and seasonal SARIMA models
- Cointegration and Error Correction Models (VECM)
- Vector Autoregressions (VAR) and impulse response functions
- Volatility modeling with ARCH and GARCH
Estimators that exploit both the cross-sectional and the time dimension.
Project: link here
Topics covered:
- Pooled OLS, Fixed Effects, and Random Effects
- The Hausman test for model selection
- First differences and within estimators
- Dynamic panels and the Arellano-Bond GMM estimator
- Difference-in-Differences (DiD), including staggered adoption (Callaway and Sant'Anna)
Gabriela De Nigris