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

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021

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 magistrale in Economics and data analysis - Enrollment from 2025/2026

The 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

ModulesCreditsTAFSSD
9
B
SECS-P/05
6
B
IUS/13
12
B
SECS-P/01
12
B
SECS-P/05 ,SECS-S/06
One module between the following
9
C
SECS-P/01
9
C
SECS-P/03
Stage
3
F
-

2° Year  activated in the A.Y. 2023/2024

ModulesCreditsTAFSSD
9
B
SECS-P/01
Two modules among the following
6
C
SECS-P/03
6
C
SECS-S/01
6
C
SECS-P/02
6
C
SECS-P/02
Two modules among the following
6
B
SECS-P/07
6
B
SECS-P/11
6
B
SECS-P/08
One module between the following
9
C
SECS-P/02
9
C
SECS-P/05
Final exam
15
E
-
ModulesCreditsTAFSSD
9
B
SECS-P/05
6
B
IUS/13
12
B
SECS-P/01
12
B
SECS-P/05 ,SECS-S/06
One module between the following
9
C
SECS-P/01
9
C
SECS-P/03
Stage
3
F
-
activated in the A.Y. 2023/2024
ModulesCreditsTAFSSD
9
B
SECS-P/01
Two modules among the following
6
C
SECS-P/03
6
C
SECS-S/01
6
C
SECS-P/02
6
C
SECS-P/02
Two modules among the following
6
B
SECS-P/07
6
B
SECS-P/11
6
B
SECS-P/08
One module between the following
9
C
SECS-P/02
9
C
SECS-P/05
Final exam
15
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
Further language skills
3
F
-
Between the years: 1°- 2°
9
D
-

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.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Econometrics  (2022/2023)

Teaching code

4S02464

Teacher

Alessandro Bucciol

Coordinator

Alessandro Bucciol

Credits

9

Language

English en

Scientific Disciplinary Sector (SSD)

SECS-P/05 - ECONOMETRICS

Period

Secondo semestre (lauree magistrali) dal Feb 20, 2023 al May 19, 2023.

Lessons timetable MoodleMoodle Seminars 0

Learning objectives

The course provides an overview of the main econometric tools, with particular emphasis on economic applications, developed interactively using professional packages. The program covers standard econometric models (OLS regression and its diagnostics) as well as more advanced models for the analysis of cross-sectional, time series and panel data (IV, probit, tobit, random and fixed effects). Particular attention will be given to the intuition behind each topic, in addition to more formal issues. Towards the end of the course a voluntary assignment will be proposed, with the aim of translating research questions into empirical analyses, applying on real data the tools learnt in class, and stimulating discussion among students. At the end of the course, students should be able to: i) read and critically interpret empirical works developed by other researchers, ii) manage small and large datasets in order to extract useful information, and iii) design and implement on their own empirical analyses based on real data.

Prerequisites and basic notions

Working knowledge of basic mathematics and statistics is recommended.

Program

1. INTRODUCTION
1a. What is Econometrics?
Definition; cross-section, time series and panel data.
1b. R tutorial
Data management; basic statistics; graphics.

2. ORDINARY LEAST SQUARES (OLS) ESTIMATOR
2a. Introduction
Univariate and multivariate regression; marginal effects and elasticity; R-squared and adjusted R-squared; outliers.
2b. Properties
Gauss-Markov assumptions; unbiasedness; efficiency; consistency; asymptotic normality.
2c. Testing
t-test on one restriction; F test on several restrictions.

3. OLS DIAGNOSTICS
3a. Specification
Collinearity; superfluous and omitted variables; RESET test of specification; Chow test of structural stability.
3b. Heteroskedasticity
White test and Breusch-Pagan test; White robust standard errors.

4. INSTRUMENTAL VARIABILE (IV) ESTIMATOR
4a. Estimator
Assumptions; Simple instrumental variable (SIV) and generalized instrumental variable (GIV); properties; two-stage derivation (2SLS).
4b. Instrument selection
Relevance test; weak instruments; Sargan validity test; Hausman exogeneity test.

5. LIMITED DEPENDENT VARIABLE (LDV)
5a. Binary dependent variable
Linear probability model (LPM); probit and logit models; marginal effects; maximum likelihood estimate; goodness of fit; hypothesis testing.
5b. Truncated and censored data
Truncated regression; tobit-I model; tobit-II and heckman models; marginal effects; goodness of fit.

6. FURTHER TYPES OF DATA
6a. Time series
Stationarity; AR processes; Durbin-Watson and Breusch-Godfrey tests; Newey-West robust standard errors.
6b. Panel data
Diff-in-diff; Pooled effects, fixed effects and random effects; goodness of fit; comparison tests; attrition.

Bibliography

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Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

Frontal teaching. Lectures include real examples interactively developed using the data management free software R.

Learning assessment procedures

The exam is made of one written essay and one individual homework. No oral integration is planned.
The written essay lasts one hour and thirty minutes and covers the whole program of the course. Use of handheld calculators is allowed, but use of personal notes or other teaching material is not allowed.
The homework is developed individually, and can be of two types (Homework I or Homework II). Homework I aims to develop analytical skills through personal data analysis. Homework II aims to develop critical skills with respect to empirical applications. Each student can choose which type of homework to deliver, but must deliver one of them. Once the deadline for delivery of Homework I has expired, it is possible to deliver Homework II only. The homework has to be delivered before taking part in the written essay; its grade remains valid throughout the academic year.

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

The written essay evaluates general understanding of the main econometric topics and the ability to understand and interpret tables reporting econometric output. The homeworks ascertain the ability to develop empirical research through personal elaboration (Homework I) or to critically comment empirical research developed by others (Homework II).

Criteria for the composition of the final grade

The final grade is given by the average of the grades in the essay and the homework, with 80% and 20% weights respectively. In order to pass the exam, it is necessary to obtain a grade not below 16/30 in the written essay. Students can separately reject the essay grade and the homework grade. However, the homework grade can be rejected only once.

Exam language

Inglese