Course Details

Statistics for economics and program evaluation

GS1090

Course
Statistics for economics and program evaluation
Code
GS1090
Academic Year
2024/2025
Curriculum Year
2023/2024
Degree Programme
ECONOMICS AND MANAGEMENT
Curriculum
A30 - ECONOMICS,MANAGEMENT AND INSTITUTIONS
Course coordinator
Lecturers
Credits
10
Lecture Hours
60
Scientific Disciplinary Sector (SSD)
SECS-S/03 - Statistics for Economics
Course Type
Single-subject learning activity
Course Delivery
OBB - Obbligatoria
Year
2
Teaching period
Primo Semestre
Campus
ALESSANDRIA
Teaching language
English
Course Contents
The course teaches the students how to apply statistical models to
analyze economic phenomena and to evaluate the impact of public
policies and public interventions. The course is taught through an ample
use of case studies and examples in order to make the technical topics
well understandable to the students.
Case studies and empirical applications involve the use of the statistical
software “STATA”.
Reference Texts
Booklets prepared by the instructor and downloadable from the DIR web
site (additional study material will be also available in English)
Learning Outcomes
By the end of the course the students should be able to implement
statistical models to analyze economic phenomena and to perform
counterfactual impact evaluations of public policies
Prerequisites
In order to best understand the topics of the course it is advisable (but
not mandatory) to have previously passed a course on inferential
statistics
Teaching Methods
In class lectures with powepoint slides and lectures in a PC lab focused on
the use of statistical software packages (STATA).
Additional Information
All the course material and information can be downloaded from the DIR
section of the DIGSPES web site
Assessment Methods
Written test that requires the students to solve problems and/or case
studies
Detailed Syllabus
Part I

This part aims at teaching students how to develop the appropriate
statistical models for analyzing economic phenomena, based on the
various types of available data.
List of topics:
1. How to use multiple regression models to produce empirical evidence
needed for public policy analysis;
2. Analysis of economic panel data: fixed effects estimators, first and long
differencing;
3. Selection and omitted variable bias
4. Non-parametric estimators, diffference in difference techiniques;
5. Shift share analysis;
6. Evaluation designs with non-esperimental data;
7. Multiple regression analysis with qualitative information (binary
dependent variables): probit and logit models

Part II

Estimation of the models of part I through the software STATA
Expected Learning Outcomes
The students are expected to be able to understand and apply the main
statistical methods to produce empirical evidence on the impacts of
public policies.
The students are also expected to posses the basic skills to programme
the statistical analyses on the STATA software
Last update:09-09-2026 00:14:31