Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea in Matematica applicata - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2023/2024
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3° Year activated in the A.Y. 2024/2025
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Legend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Econometrics (2024/2025)
Teaching code
4S01951
Academic staff
Coordinator
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
SECS-P/05 - ECONOMETRICS
Period
Semester 1 dal Oct 1, 2024 al Jan 31, 2025.
Courses Single
Authorized
Learning objectives
Statistic tools and economic theory will be applied in order to provide students with capabilities to understand and perform empirical analysis of economic phenomena. Empirical problems and applications will be discussed during the course to provide students with the tools needed for the analysis of economic data.
Prerequisites and basic notions
We require basic knowledge of calculus. The course material relies on prior knowledge of basic statistics and probability theory.
Program
1. INTRODUCTION (Stock-Watson, ch.2-3)
1.1. What is econometrics?
1.2. Probability
1.3. Statistics
2. REGRESSION ANALYSIS (Stock-Watson, ch.4-9)
2.1. Linear regressione with a single regressor and hypothesis testing
2.2. Linear regression with multiple regressions and hypothesis testing
2.3. Diagnostics of the regression model: specification, heteroskedasticity, autocorrelation
3. EXTENSIONS (Stock-Watson, ch.11-12)
3.1. Regression with instrumental variables
3.2. Regression with binary dependent variable
Bibliography
Didactic methods
This module is composed of 52 hours of frontal lectures and exercises. During the semester students will be given problem sets to attempt at home to encourage systematic studying and self-feedback.
Learning assessment procedures
A two hour final exam
Evaluation criteria
Correctness of the results and careful derivation and explanation
Criteria for the composition of the final grade
The final exam will carry 100% of the final grade
Exam language
Italiano