Econometrics

Syllabus

• Review of probability and statistics concepts.
• Regression estimation and inference under different assumptions.
• Small-sample and large-sample properties of regression estimators.
• Heteroskedasticity, robust inference and related regression diagnostics.
• Functional form in regression models.
• Categorical explanatory variables and interpretation of regression coefficients.
• Prediction, model selection and basic tools from statistical learning.
• Binary dependent variable models: the linear probability model, logit and probit.
• Classification and interpretation of nonlinear probability models.
• Further topics in econometrics: multinomial logit models and instrumental variables.

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