Ateneo - UPO

This module provides basic skills on main statistical tools that can be adopted to model economic phenomenon, make forecasts, test hypothesis. The goal of the module is to introduce students to theoretical and practical aspects (also using STATA) on basic econometric techniques for data analysis. The skills fostered can be used to perform empirical research. The statistical techniques proposed are applied to case studies concerning various dimensions of sustainable development (environmental, economic, social).

Carter Hill, R., Griffiths, W.E., Lim, G.C. (2013) “Principi di econometria”, Zanichelli, Bologna. Chapter 1, Piccolo manuale di probabilità, 2, 3, 4, 5, 16.

Details on specific pages of the textbook to be considered are in the slides uploaded on the DIR platform.

Slides and other material will be provided on the DIR platform.

The module aims to foster theoretical and technical skills in the analysis and processing of data that can be applied in many fields to produce knowledge. Students will be able to recognize appropriate techniques to run basic econometric models, particularly linear regression. They will learn tools to model the relation between variables, make forecasts, perform hypothesis tests.

None

The module uses different teaching methods that will alternate. Among these modalities: face-to-face lectures; assignments in itinere; discussion of scientific articles (also hosting speakers); computer lab using statistical software (STATA).

Attendance is suggested.

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/services-students-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.

For students "with active participation".

The students who:

i) Participate in at least 70% of lectures;

ii) Participate in in-class exercises;

iii) actively participate in offered scientific seminars;

iv) develop, implement and present the project work (the project work will be presented in the classroom):

earn 50% of the final grade of the module. Evaluation is at the end of the module. The remaining part of the vote of the module (50%) comes from a written exam with 2 open questions (1 hour available).

Verification of learning (in the usual 0-30L scale) will take into account the ability of students to solve in-class exercises; develop, carry out and communicate the conclusions of research project; reproduce (in the written exam) the topics (theoretical) and solve exercises on the covered program.

For students “without active participation”.

The exam is written. The exam has 4 open questions (2 hours available).

Verification of learning (in the usual 0-30L scale) will take into account the students' ability to reproduce the topics (theoretical) and and solve exercises on the covered program.

An introduction to econometrics

Little handbook of probability

The simple linear regression

Interval estimation and hypothesis testing

Forecasting and data adaptation.

The multivariate linear regression

Models with binary dependent variables

It is assumed that students will acquire theoretical and technical skills (using STATA) to conduct sound empirical research. Participants will learn how to model economic problems, collect information on available database, choose and run statistical models.
The application of the knowledge to real data and going through scientific literature will help students to grasp how the use of data can allow to produce knowledge (for example concerning the magnitude of the relation between variables, make forecasts or test hypotheses). The contents of the module are applied to the multiple facets of "sustainable development" (environmental, economic, social dimensions) providing a multi-trans-disciplinary vision of the application of the concepts learned.