ECONOMETRICS

This course is one of the core teaching activities of the PhD program 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 specifically, 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 and Gretl.

Knowledge and competencies:
- 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 analyzed 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. Classic Econometric Models

A.1 Regression Models
A.2 The classical hypothesis
A.3 The Statistical Properties of Ordinary Least Squares
A.4 The Frisch-Waugh-Lowell theorem
A.5 Hypothesis Testing in Linear Regression Models
A.6 Asymptotic results
A.7 Generalized Least Squares and Related Topics
A.8 A primer on bootstrap

B. Time Series Econometrics

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
F. Hayashi, Econometrics, Princeton University Press, 2000.

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

Additional references:
- Lectures slides and additional material will be made available on Moodle during the course

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.

Homeworks and 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.

Overall course grade
The exam is considered passed with the achievement of 18 total points out of 30.

Lectures, classes, empirical applications on economic and/or financial data using econometric software. Students will be encouraged to solve and to hand in some pieces of homework throughout the course and to work in groups on empirical projects

Inglese

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