Teaching code

4S01951

Teacher

Francesca Rossi

Coordinator

Francesca Rossi

Credits

9

Language

Italian

Scientific Disciplinary Sector (SSD)

SECS-P/05 - ECONOMETRICS

Period

Primo semestre L dal Sep 23, 2024 al Jan 10, 2025.

Courses Single

Authorized

Lessons timetable Moodle Seminars 0

Learning objectives

The course provides the main econometric tools to develop, based on the available data, an empirical analysis on the relationship between economic variables and to correctly interpret and use the results obtained. In fact, many economic decisions require quantitative answers to quantitative questions, and decisions based on empirical evidence are generally considered more helpful and effective.
The course uses a scientific language based on deductive reasoning. Technical aspects of econometrics, however, will be introduced only when necessary, whereas key attention will be given to the development of an intuitive comprehension of the material, in such a way to allow for an effective and creative use of the acquired knowledge.
At the end of the course the student is expected to (a) have critical skills with respect to empirical applications made by others and (b) be able to autonomously set up and run empirical analyses in the broad areas of economics and finance.

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.10-12)
3.1. Regression with instrumental variables
3.2. Regression with binary dependent variable
3.3 Regression with panel data

Bibliography

Didactic methods

This module is composed of 72 hours of frontal lectures. During the semester students will be given problem sets to attempt at home to encourage systematic studying and self-feedback. In addition, a designated tutor will present additional questions and empirical problems to equip students with knowledge of the software Gretl.

Learning assessment procedures

The exam is made of one written exam and one individual homework. In order to pass the exam, it is necessary to obtain a grade not below 18/30 in the written exam.
The written exam lasts two hours and covers the whole program of the module. It is possible to use a calculator, but neither notes nor other teaching material. Students have the option of taking the exam in two parts, the first one during the week of 7-11 November (dates to be confirmed), the second part during the first exam session in January. Details of the topics covered in the first half of the exam will be made available on Moodle in due course. The passing mark for the first part is set at 16/30.
The final grade is the average of the grades in the two parts.
The homework can be of two types (Homework 1 and Homework 2). Each student is free to choose either Homework 1 or Homework 2 but must deliver one of them. Once the deadline for delivery of Homework 2 has expired, it is possible to deliver Homework 1 only. The homework grade remains valid throughout the academic year.
Homework 1
The homework aims to develop critical skills with respect to empirical applications. Each student is free to choose one article from www.lavoce.info, www.voxeu.org/, www.ilsole24ore.com or other webiste, provided that it discusses an economic topic and makes use of data.
The homework consists in an essay of max. 2000 words, to be delivered to the address diego.lubian[at]univr.it within the day in which the exam is scheduled. The homework will pass through an antiplagiarism analysis by means of the Compilation software; it is advisable to make a personal preliminary analysis before submitting the homework.
The essay must be divided in sections in such a way to contain a) a reference to the chosen article (title, authors, link), b) a summary of the article, briefly describing its motivation, goal, methodology and results, and c) a critical comment on the methodology, also proposing alternative analyses and possible future developments. The essay must also report the word count.
Homework 2
The homework aims to develop analytical skills through personal data analysis in Gretl. Any student interested in this homework must write to the address diego.lubian[at]univr.it communicating name, surname and ID number. He or she will then receive a number, corresponding to the dataset to be used. The text of the homework will be made available at the end of the lectures; the solution must be delivered by email within the following three days.

Evaluation criteria

To obtain full marks, students should show knowledge of the various econometric methodologies to understand and solve the diverse issues posed by regression models.

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 75% and 25% weights respectively.

Exam language

Italiano