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
teacher profile teaching materials
Further topics include maximum likelihood estimation, hypothesis testing, and techniques to address violations of classical model assumptions (heteroskedasticity, autocorrelation, multicollinearity, measurement errors). The course discusses generalized least squares, diagnostic tests, and instrumental variable estimators.
It continues with linear forecasting, model misspecification (omitting relevant or including redundant variables), and the use of dummy variables to test regression stability (Chow test). Measures of model fit such as R², AIC, and BIC are introduced, along with distributed lag models.
The final part of the course focuses on panel data models (fixed and random effects) and time series analysis, covering descriptive aspects, structural components (trend, cycle, seasonality), and stochastic models (AR, MA, ARMA), emphasizing stationarity and invertibility.
James H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Programme
The course covers econometrics and statistical modeling, beginning with a review of matrix algebra and estimation theory. It introduces the classical linear regression model, exploring its assumptions, parameter estimation via Ordinary Least Squares (OLS), and the Gauss-Markov theorem.Further topics include maximum likelihood estimation, hypothesis testing, and techniques to address violations of classical model assumptions (heteroskedasticity, autocorrelation, multicollinearity, measurement errors). The course discusses generalized least squares, diagnostic tests, and instrumental variable estimators.
It continues with linear forecasting, model misspecification (omitting relevant or including redundant variables), and the use of dummy variables to test regression stability (Chow test). Measures of model fit such as R², AIC, and BIC are introduced, along with distributed lag models.
The final part of the course focuses on panel data models (fixed and random effects) and time series analysis, covering descriptive aspects, structural components (trend, cycle, seasonality), and stochastic models (AR, MA, ARMA), emphasizing stationarity and invertibility.
Core Documentation
Introduzione all’econometriaJames H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Type of evaluation
Oral exam on the course topics teacher profile teaching materials
Further topics include maximum likelihood estimation, hypothesis testing, and techniques to address violations of classical model assumptions (heteroskedasticity, autocorrelation, multicollinearity, measurement errors). The course discusses generalized least squares, diagnostic tests, and instrumental variable estimators.
It continues with linear forecasting, model misspecification (omitting relevant or including redundant variables), and the use of dummy variables to test regression stability (Chow test). Measures of model fit such as R², AIC, and BIC are introduced, along with distributed lag models.
The final part of the course focuses on panel data models (fixed and random effects) and time series analysis, covering descriptive aspects, structural components (trend, cycle, seasonality), and stochastic models (AR, MA, ARMA), emphasizing stationarity and invertibility.
James H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Programme
The course covers econometrics and statistical modeling, beginning with a review of matrix algebra and estimation theory. It introduces the classical linear regression model, exploring its assumptions, parameter estimation via Ordinary Least Squares (OLS), and the Gauss-Markov theorem.Further topics include maximum likelihood estimation, hypothesis testing, and techniques to address violations of classical model assumptions (heteroskedasticity, autocorrelation, multicollinearity, measurement errors). The course discusses generalized least squares, diagnostic tests, and instrumental variable estimators.
It continues with linear forecasting, model misspecification (omitting relevant or including redundant variables), and the use of dummy variables to test regression stability (Chow test). Measures of model fit such as R², AIC, and BIC are introduced, along with distributed lag models.
The final part of the course focuses on panel data models (fixed and random effects) and time series analysis, covering descriptive aspects, structural components (trend, cycle, seasonality), and stochastic models (AR, MA, ARMA), emphasizing stationarity and invertibility.
Core Documentation
Introduzione all’econometriaJames H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Type of evaluation
Oral exam on the course topics teacher profile teaching materials
Further topics include maximum likelihood estimation, hypothesis testing, and techniques to address violations of classical model assumptions (heteroskedasticity, autocorrelation, multicollinearity, measurement errors). The course discusses generalized least squares, diagnostic tests, and instrumental variable estimators.
It continues with linear forecasting, model misspecification (omitting relevant or including redundant variables), and the use of dummy variables to test regression stability (Chow test). Measures of model fit such as R², AIC, and BIC are introduced, along with distributed lag models.
The final part of the course focuses on panel data models (fixed and random effects) and time series analysis, covering descriptive aspects, structural components (trend, cycle, seasonality), and stochastic models (AR, MA, ARMA), emphasizing stationarity and invertibility.
James H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Programme
The course covers econometrics and statistical modeling, beginning with a review of matrix algebra and estimation theory. It introduces the classical linear regression model, exploring its assumptions, parameter estimation via Ordinary Least Squares (OLS), and the Gauss-Markov theorem.Further topics include maximum likelihood estimation, hypothesis testing, and techniques to address violations of classical model assumptions (heteroskedasticity, autocorrelation, multicollinearity, measurement errors). The course discusses generalized least squares, diagnostic tests, and instrumental variable estimators.
It continues with linear forecasting, model misspecification (omitting relevant or including redundant variables), and the use of dummy variables to test regression stability (Chow test). Measures of model fit such as R², AIC, and BIC are introduced, along with distributed lag models.
The final part of the course focuses on panel data models (fixed and random effects) and time series analysis, covering descriptive aspects, structural components (trend, cycle, seasonality), and stochastic models (AR, MA, ARMA), emphasizing stationarity and invertibility.
Core Documentation
Introduzione all’econometriaJames H. Stock - Mark W. Watson
Ed. Pearson
Econometria
Marno Verbeek
Ed. Zanichelli
Lecturer's Notes
Type of evaluation
Oral exam on the course topics