ID:
509110
Duration (hours):
60
CFU:
9
SSD:
POLITICA ECONOMICA
Year:
2025
Overview
Date/time interval
Primo Semestre (29/09/2025 - 12/12/2025)
Syllabus
Course Objectives
Econometrics emerges from the intersection between economics and statistics, and deals—literally—with measuring economic phenomena. Although traditionally associated with economics, it has now become a fundamental tool for empirical analysis in all the social sciences. Disciplines such as sociology, political science, psychology, international relations, and demography increasingly rely on econometric techniques to rigorously study human behavior, causal relationships among complex phenomena, the effectiveness of public policies, and the evolution of collective opinions and attitudes.
At the core of econometrics lies the analysis of relationships between variables, with the aim of identifying correlations, estimating causal effects, forecasting the future evolution of uncertain phenomena, and testing theoretical hypotheses in light of data.
The practical applications of econometrics are numerous and constantly expanding. The quantitative methods introduced in this course make it possible, for instance, to assess the impact of public subsidies on the labor market, analyze the effectiveness of anti-poverty policies, understand the factors influencing voter turnout or access to education, and build predictive models that inform the decisions of governments, international institutions, or private organizations. Thanks to their versatility, these techniques are used in a wide variety of contexts, making them today an indispensable tool for anyone wishing to analyze the social world through data.
This course aims to provide students with the essential tools of econometric analysis, with a dual objective: on the one hand, to learn how to read and critically interpret the results of econometric studies published in scientific journals and in reports by public institutions, national agencies, or international organizations; on the other hand, to acquire the skills needed to independently conduct simple empirical analyses using dedicated software, and to be prepared for more advanced courses at the master’s or doctoral level.
The approach will be practice-oriented and application-driven: greater emphasis will be placed on the economic and logical intuition behind the results rather than on mathematical formalization or technical details, which will be kept to the minimum necessary.
At the core of econometrics lies the analysis of relationships between variables, with the aim of identifying correlations, estimating causal effects, forecasting the future evolution of uncertain phenomena, and testing theoretical hypotheses in light of data.
The practical applications of econometrics are numerous and constantly expanding. The quantitative methods introduced in this course make it possible, for instance, to assess the impact of public subsidies on the labor market, analyze the effectiveness of anti-poverty policies, understand the factors influencing voter turnout or access to education, and build predictive models that inform the decisions of governments, international institutions, or private organizations. Thanks to their versatility, these techniques are used in a wide variety of contexts, making them today an indispensable tool for anyone wishing to analyze the social world through data.
This course aims to provide students with the essential tools of econometric analysis, with a dual objective: on the one hand, to learn how to read and critically interpret the results of econometric studies published in scientific journals and in reports by public institutions, national agencies, or international organizations; on the other hand, to acquire the skills needed to independently conduct simple empirical analyses using dedicated software, and to be prepared for more advanced courses at the master’s or doctoral level.
The approach will be practice-oriented and application-driven: greater emphasis will be placed on the economic and logical intuition behind the results rather than on mathematical formalization or technical details, which will be kept to the minimum necessary.
Course Prerequisites
It is preferable for students to have a basic understanding of statistical concepts. For those who do not possess this background, specific readings will be suggested. During the first classes, and whenever necessary, the instructor will review the key mathematical and statistical notions relevant for the course.
Teaching Methods
Classes will alternate between theoretical explanations, practical exercises using STATA software, and critical discussions of the obtained results. Applied activities will make use of real data drawn from national and international databases, selected for their relevance to the topics addressed throughout the degree program.
Assessment Methods
Given the nature and objectives of the course, regular and active attendance is strongly recommended. Teaching materials and exam content are the same for all students, whether attending or not; however, the assessment methods differ accordingly.
Non-attending students
Non-attending students will take a written exam consisting of six questions covering the entire syllabus. They are required to answer five out of six questions.
Attending students
Students who attend classes on a regular basis will have the opportunity to engage in periodic in-class assessments, consisting of short theoretical questions or simple exercises. These are designed to monitor students’ progress throughout the semester and to support their preparation for the final exam.
Attending students will also be asked to prepare and present a short project or paper, either individually or in small groups. The assignment involves the use of real data and the practical application of the theoretical models discussed in class.
The specific procedures for the in-class tests and for the project presentation will be defined and communicated at the beginning of the course, depending on the number of attending students.
Students opting for this assessment format can earn up to 12 additional points through the in-class activities and final project, and will be required to answer only three out of six questions in the written exam.
The attending-student exam format is available exclusively for those taking the written exam during the three sessions of the winter exam period (January–February 2026).
Non-attending students
Non-attending students will take a written exam consisting of six questions covering the entire syllabus. They are required to answer five out of six questions.
Attending students
Students who attend classes on a regular basis will have the opportunity to engage in periodic in-class assessments, consisting of short theoretical questions or simple exercises. These are designed to monitor students’ progress throughout the semester and to support their preparation for the final exam.
Attending students will also be asked to prepare and present a short project or paper, either individually or in small groups. The assignment involves the use of real data and the practical application of the theoretical models discussed in class.
The specific procedures for the in-class tests and for the project presentation will be defined and communicated at the beginning of the course, depending on the number of attending students.
Students opting for this assessment format can earn up to 12 additional points through the in-class activities and final project, and will be required to answer only three out of six questions in the written exam.
The attending-student exam format is available exclusively for those taking the written exam during the three sessions of the winter exam period (January–February 2026).
Texts
Wooldridge J.M., Introductory Econometrics. A Modern Approach, 5th edition, Cengage, 2012
Contents
Wooldridge J.M., Introductory Econometrics. A Modern Approach, 5th edition, Cengage, 2012:
CHAPTER 1 The Nature of Econometrics and Economic Data
CHAPTER 2 The Simple Regression
Model
CHAPTER 3 Multiple Regression
Analysis: Estimation
CHAPTER 4 Multiple Regression
Analysis: Inference
CHAPTER 5 Multiple Regression
Analysis: OLS Asymptotics
CHAPTER 6 Multiple Regression
Analysis: Further Issues
CHAPTER 7 Multiple Regression
Analysis with Qualitative
Information: Binary (or Dummy)
Variables
CHAPTER 8 Heteroskedasticity
CHAPTER 9 More on Specification
and Data Issues
CHAPTER 1 The Nature of Econometrics and Economic Data
CHAPTER 2 The Simple Regression
Model
CHAPTER 3 Multiple Regression
Analysis: Estimation
CHAPTER 4 Multiple Regression
Analysis: Inference
CHAPTER 5 Multiple Regression
Analysis: OLS Asymptotics
CHAPTER 6 Multiple Regression
Analysis: Further Issues
CHAPTER 7 Multiple Regression
Analysis with Qualitative
Information: Binary (or Dummy)
Variables
CHAPTER 8 Heteroskedasticity
CHAPTER 9 More on Specification
and Data Issues
Course Language
Italian
Degrees
Degrees
ECONOMIC DEVELOPMENT AND INTERNATIONAL RELATIONS
Master’s Degree
2 years
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People
People
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