Università Roma Tre

THE MULTIPLE LINEAR REGRESSION MODEL. INTERPRETATION AND COMPARISON OF REGRESSION MODELS. HETEROSCHEDASTICITY AND AUTOCORRELATION. ENDOGENITY AND INSTRUMENTAL VARIABLES.

MODELS WITH LIMITED DEPENDENT VARIABLES. BINARY CHOICE MODELS: PROBIT AND LOGIT MODELS. MULTIPLE-CHOICE MODELS: PROBIT AND LOGIT MODELS ORDERED, LOGIT MODEL MULTINOMIAL. MODELS FOR COUNTING DATA. TOBIT MODELS.
Seemingly Unrelated Regression (SUR): ESTIMATION AND TESTING METHODS.
SIMULTANEOUS EQUATIONS Model (SEM).
MODELS FOR PANEL DATA. THE STATIC LINEAR MODEL: FIXED EFFECT MODELS; RANDOM EFFECT MODELS. ESTIMATORS WITHIN AND BETWEEN. HETEROSCHEDASTICITY AND AUTOCORRELATION TESTS. DYNAMIC MODELS FOR PANEL DATA: THE ARELLANO-BOND ESTIMATOR.
MODELS WITH LAGGED VARIABLES: DYNAMIC REGRESSION MODELS, MODELS WITH DISTRIBUTED LAGS (DL, ARDL).

For the course program students can consult the following texts (the first three can be considered alternative among them)
1) Introduzione all'Econometria, J. H. Stock, M. W. Watson, Ed. Pearson
2) Econometrica, J. Johnston, Ed. Franco Angeli
3) Econometria, M. Verbeek, Ed. Zanichelli
4) Lectures on advanced econometrics, L. Pieraccini, Ed. Aracne
5) Introduction to Time Series Analysis and Forecasting, D. C. Montgomery, C. L. Jennings, M. Kulahci, Ed. Wiley

Some of the topics in the course can also be found in
Introductory Econometrics for Finance, C. Brooks, Cambridge University Press

For introductory references of statistical inference and algebra of matrices students can consult:
Fondamenti di Inferenza Statistica, L. Pieraccini, Ed. Giappichelli
Matrix Differential Calculus in Statistics and Econometrics, J. R. Magnus, H. Neudecker, Ed. Wiley Series in Probability and Statistics

For the laboratory course with R, students can consult one of the following texts:
Introductory Statistics with R, P. Dalgaard, Ed. Springer
An Introduction to Applied Multivariate Analysis with R, B. Everitt, T. Hothorn, Ed. Springer