# ECONOMETRICS

2020/2021
Official course title
ECONOMETRICS
Course code
EM2Q05 (AF:346422 AR:178656)
Modality
On campus classes
ECTS credits
7
Degree level
Corso di Dottorato (D.M.45)
Educational sector code
SECS-P/05
Period
2nd Semester
Course year
1
Where
VENEZIA
Moodle
Go to Moodle page
This course is one of the core teaching activities of the PhD progam in "Economics" and of the course "Economia e Finanza - QEM". In line with the educational objectives of the course, this activity aims to present the main mathematical and statistical tools necessary for the analysis of economic phenomena; particular attention will be devoted to the use of formal language and methodological rigor. More specifially, the course aims to complete students preparation in Econometrics by being able to deal with advanced econometric models and methods. Moreover, it will provide students with the main econometric methods, with special reference to the analytical derivation of the estimators and to inference procedures. The course is well equipped with econometric practice, enhancing practical abilities in the use of the econometric software such as Matlab, STATA, E-Views and Gretl.
Knowledge and competences:
- sound knowledge of the theoretical foundations of econometric methods
- specification and formal derivation of econometric models based on economic models
- investigate, understand and interpret economic and financial phenomena, by means of up-to-data econometric tools

Application of acquired knowledge and skills:
- ability to exploit up-to-date analytical tools and formal derivations to gain insights on relevant economic relationships
- interpretation and management of economic dynamics, through the use of advanced analytical tools
- being able to design empirical strategies to measure and quantify economic phenomena and relationships among economic variables

Judgement and interpretation skills:
- evaluate strengths and weaknesses of the methodologies analysed and of their empirical application
- being able to critically interpret the outcomes of empirical analyses
Mathematical Tools:
Matrix Algebra
Differential Calculus
Integral Calculus

Statistical Tools:
Random Variables and Distribution Theory
Point and Interval Estimation
Hypothesis Testing
Least Squares and Standard Linear Model
A. First Part
A.1 Regression Models
A.2 The Geometry of Linear Regression
A.3 The Statistical Properties of Ordinary Least Squares
A.4 Hypothesis Testing in Linear Regression Models
A.5 Confidence Intervals
A.6 Nonlinear Regression
A.7 Generalized Least Squares and Related Topics

B. Second Part
B.1 Stochastic Processes
B.2 Asymptotic Theory
B.3 Stationary ARMA processes
B.4 Stationary Vector Processes
B.5 Non-stationary Processes
B.6 Cointegration
B.7 State-space Models
First Part
Russell Davidson and James MacKinnon, Econometric Theory and Methods, Oxford University Press, 2004.

Second Part
James Hamilton, Time Series Analysis, Princeton University Press, 1994.

- Lectures slides and additional material will be made available on Moodle during the course
Part A
Written discussion of the estimation results and analytical solutions of advanced econometric problems. Homeworks will be assigned during the course and then discussed during the practice sessions.

Part B
The exam consists in individual and group assignments, and in the preparation and presentation of a final project. The exam is evaluated on a 30-point basis. The solution of the assignments can yield up to 20 points out of 30 and the final project can yield up to 10 points out of 30.

In both Part A and B the homework and the assignments are intended to verify the progress in the learning activity and the abilities to go deep autonomously to the heart of the topics of the course.
The assignments consist of problems to solve and questions to reply regarding additional reading material properly referenced in the text of assignments. The final project develops or extends further the topics of the course and includes an original contribution of the student, such as new models, analysis of their properties, or original applications to real data. The project preparation aims at putting into practice the knowledge acquired. The oral presentation of the project aim at verifying the level of knowledge of the topics in the projects and the ability to communicate them in a clear and rigorous way.