ECONOMETRICS

Degree course: 
Corso di First cycle degree in ECONOMICS AND MANAGEMENT
Academic year when starting the degree: 
2018/2019
Year: 
3
Academic year in which the course will be held: 
2020/2021
Course type: 
Optional subjects
Language: 
Italian
Credits: 
6
Period: 
First Semester
Standard lectures hours: 
40
Detail of lecture’s hours: 
Lesson (40 hours)
Requirements: 

No previous knowledge is required.

The test is a 1.5-hour written examination based on 8-10 exercises of varying difficulty and score. Some (4/5) questions are closed-ended, the others are open-ended. Wrong answers do not lead to a loss of score. The exam prizes the ability to reason critically and to justify the proposed solution.

Assessment: 
Voto Finale

The advent of computers and of the Internet has led to the availability of a huge amount of data, in a form and quantity that was not even thinkable until some years ago. This phenomenon, sometimes called 'big data' or 'data deluge', is going to bring about the prophecy of H. G. Wells, that the ability “to think in averages and maxima and minima” will be in the future as necessary “as it is now to be able to read and write.” This is a fortiori true in economics. As such, the course aims at introducing the student to the statistical study of the relations among economic phenomena.

At the end of the course, it is expected that the student is able to
- interpret the results of a linear regression model,
- conduct tests and
- make forecasts.

The study of economic relations.
The linear regression model. The linear specification.
The least squares estimator and its properties; use for forecasting.
Effects of (partial or total) violation of the assumptions. Heteroskedasticity and autocorrelation.
Qualitative variables.
Decisional and previsional use of estimates.

The study of economic relations.
The linear regression model. The linear specification.
The least squares estimator and its properties; use for forecasting.
Effects of (partial or total) violation of the assumptions. Heteroskedasticity and autocorrelation.
Qualitative variables.
Decisional and previsional use of estimates.

Slides written by the instructor.

Convenzionale

The course is composed of lectures taught with the support of a set of slides (in English) and programming exercises with the statistical software R. During the class, students are invited to interact with the instructor.

Office hours by appointment arranged by email.

Professors