The course aims to introduce the main techniques of econometrics, the use of which has become common practice in empirical work in many areas of economic, financial and business analysis. The focus is on the intuition behind the different approaches and their practical relevance. The course introduces and discusses empirical examples and applications from areas of analysis such as labour economics, finance, international economics, environmental economics, macroeconomics and management. The use of the different procedures is illustrated by practical examples based on the use of data taken from real cases, with the use of a suitable software (e-views, r).
Curriculum
scheda docente
materiale didattico
Fixed-effects and random-effects panel data models. Time series analysis: descriptive aspects, AR, MA, ARMA models, distributed lag models.
James H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Programma
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.
Testi Adottati
Introduzione all’econometriaJames H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Modalità Erogazione
Classroom lecturesModalità Valutazione
Oral interview on course topics
scheda docente
materiale didattico
Fixed-effects and random-effects panel data models. Time series analysis: descriptive aspects, AR, MA, ARMA models, distributed lag models.
James H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Programma
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.
Testi Adottati
Introduzione all’econometriaJames H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Modalità Erogazione
Classroom lecturesModalità Valutazione
Oral interview on course topics
scheda docente
materiale didattico
Fixed-effects and random-effects panel data models. Time series analysis: descriptive aspects, AR, MA, ARMA models, distributed lag models.
James H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Programma
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.
Testi Adottati
Introduzione all’econometriaJames H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Modalità Erogazione
Classroom lecturesModalità Valutazione
Oral interview on course topics