Ateneo - UPO

The course provides students with the knowledge to set up linear regression econometric models for the estimation of causal connections.

1. J.H. Stock, M.W. Watson, Introduzione all'Econometria 5a Ed., Pearson
2. Notes of the teacher

The goal of the course is to enable students to set the appropriate econometric models on the basis of the available data and the empirical evidence.

The basic knowledge of statistics, that can be acquired with a basic course in Statistics, is given as known.
Therefore, it is recommended to carry out the "Brief pre-course in Statistics" on the DIR page of the course before attending the course.

The organization of the course includes lectures supported by powerpoint slides which present case studies. Applications will be performed using statistical softwares (Gretl) and spreadsheets (EXCEL).

Students with physical disabilities, Learning Disabilities or Special Education Needs can request specific services and tools via the Staff Sviluppo e Coordinamento Carriere e Servizi alle Studentesse e agli Studenti, consulting the University webpage: https://www.uniupo.it/en/services/servicesstudents-physical-or-learning-disabilities Students with disabilities, learning disabilities or special education needs, once they have contacted the University Staff, can refer to the tutor in charge of the course to define the examination modalities, concerning academic aspects.

Written examination oriented to the solution of economic problems by econometric models.

1. What is econometrics;
2. Preliminary notions: observational data and experimental data, population and sample; marginal distributions and conditional distributions;
3. The simple linear regression model;
4. The ordinary least squares method (OLS);
5. Inference in simple regression;
6. The problem of omitted variable bias and the multiple regression model;
7. The OLS and the problem of multicollinearity
8. The interpretation of the regression coefficients: the scale of the independent variables and the dependent variable;
9. Inference in multiple regression;
10. Regression with binary dependent variable;
11. Regression models for time-series forecasting;
12 Regression with panel data (no 5 cfu- course).

Ability to define a suitable regression model and to read and interpret model estimation results.