• Introduction: Microeconometrics and causality
• Fundamentals: The multiple linear regression model, including
tests and prediction
• Regression methods for time series data (e. g. decomposition,
exogenous effects, FGLS)
• Discrete choice models (e.g. binary, ordered and multinomial
logit/probit)
• Methods for causal analysis (e.g. instrumental variables, two stage
least squares, difference-in-difference, regression discontinuity
design, matching)
• Panel data models (e.g. random effects, fixed effects)
• Sample selection bias and Heckman correction