Programme

Basic knowledge of inference and linear algebra. Classical linear regression model: basic model assumptions, least squares estimation, maximum likelihood estimation, testing of model parameters, linearity, heteroschedasticity, autocorrelation, multicollinearity, endogenous regressors and estimators to instrumental variables, linear prediction, misspecification, stability of regression function.
Fixed-effects and random-effects panel data models. Time series analysis: descriptive aspects, AR, MA, ARMA models, distributed lag models.

Core Documentation

Introduzione all’econometria
James H. Stock - Mark W. Watson
Ed. Pearson

Econometria
Marno Verbeek
Ed. Zanichelli

Lecturer's Notes

Type of delivery of the course

Classroom lectures

Type of evaluation

Oral interview on course topics

Programme

Basic knowledge of inference and linear algebra. Classical linear regression model: basic model assumptions, least squares estimation, maximum likelihood estimation, testing of model parameters, linearity, heteroschedasticity, autocorrelation, multicollinearity, endogenous regressors and estimators to instrumental variables, linear prediction, misspecification, stability of regression function.
Fixed-effects and random-effects panel data models. Time series analysis: descriptive aspects, AR, MA, ARMA models, distributed lag models.

Core Documentation

Introduzione all’econometria
James H. Stock - Mark W. Watson
Ed. Pearson

Econometria
Marno Verbeek
Ed. Zanichelli

Lecturer's Notes

Type of delivery of the course

Classroom lectures

Type of evaluation

Oral interview on course topics