Module Details

Environment, law and economy of development: Applied economics of sustainable development

MF0600

Course
Environment, law and economy of development: Applied economics of sustainable development
Code
MF0600
Academic Year
2023/2024
Curriculum Year
2022/2023
Degree Programme
ENVIRONMENTAL STUDIES AND SUSTAINABLE DEVELOPMENT
Curriculum
A001 - GENERICO
Course coordinator
Lecturers
Credits
6
Lecture Hours
48
Scientific Disciplinary Sector (SSD)
SECS-P/06 - Applied Economics
Course Type
Single-subject learning activity
Course Delivery
OBB - Obbligatoria
Year
2
Teaching period
Secondo Semestre
Campus
VERCELLI
Teaching language
Italian
Course Contents
This module provides students with 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 understand, critically evaluate and autonomously carry out sound empirical research. The statistical techniques proposed are applied to case studies concerning various dimensions of sustainable development (environmental, economic, social).
Reference Texts
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.
Learning Outcomes
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.
Prerequisites
None
Teaching Methods
The module uses different teaching methods that will alternate. Among these modalities: face-to-face lectures;exercises; computer lab using statistical software (STATA) (12 hours); discussion of scientific articles (also hosting speakers).
Additional Information
Attendance is suggested.
Assessment Methods
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 (using STATA) and present the project work. The project work will be presented in the classroom;

earn 50% of the final grade of the module. Assesment is in itinere. The remaining part of the vote of the module (50%) is represented by 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) covered with the 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) covered with the program.
Detailed Syllabus
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
Expected Learning Outcomes
It is assumed that students will acquire theoretical and technical skills (statistical sotware, 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) providing a multi-trans-disciplinary vision of the application of the concepts learned.
Last update:09-09-2026 00:14:31