1. The nature of Econometrics and typology of economic data.
2. Simple linear regression model and introduction on estimation methods: OLS, GMM and ML
3. Multiple linear regression model: statistical inference and testing of hypothesis
4. Multiple regression and functional forms
5. Linear regression with dummy variables
6. Cross-sectional correlation and an introduction to spatial econometrics models
7. Introduction on panel data
8. Introduction to the R software environment.

1. What is econometrics and steps in the empirical analysis.
2. Structure of economic data: cross-section, time series and panel data 3. Definition and derivation of the simple linear regression model:
- Properties of OLS
- Units of measurements and functional form
- Expected value and variance of the OLS
4. Definition and derivation of the simple linear regression model:
- Mechanics and interpretation of the coefficients
- Comparison between single and multiple regression model
- Expected value and variance of the OLS.
- Efficiency of the OLS and the Gauss-Markov theorem
5. Inference of the linear regression model
- Hypothesis test on a single coefficient
- Testing hypothesis about a single linear combination of the parameters
- Testing multiple linear restrictions
6. More on the multiple regression model
- Effects of data scaling
- Functional form: Logs, quadratic form and interactions between variables
7. Linear regression using dummy variables: models with a single dummy, models with multiple dummies, binary dependent variable models
8. Introduction to spatial models
- Definition of the spatial weights matrix
- Specification of spatial models: SARAR, LAG, ERROR, MIXED etc.
- Estimation methods for spatial models: Instrumental variables and Maximum likelihood
9. Panel data models: pooled, fixed effects and random effects