Contribution of the course to the overall degree programme goals
This course is one of the teaching activities of the PhD progam in "Economics". 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 programming languages such as MATLAB and R.
Expected learning outcomes
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
Random Variables and Distribution Theory
Point and Interval Estimation
Least Squares and Standard Linear Model
1. Decision Theoretical Fundation of Statistics
2. Bayesian inference
3. Numerical methods for for posterior approximation
4. Bayesian nonparametric methods
5. Bayesian Linear Regression.
6. Probit and Logit models. Truncation and censoring. Models for count data.
7. Bayesian SUR and VAR
8. Bayesian Latent Variable Models
9. State-space models (Kalman filter, Hamilton filter, particle filter)
Notes and slides.
Koop, G., Dale J. P., Tobias, J. L. (2007) Bayesian Econometric Methods, Cambridge University Press
Lancaster, T. (2004) An Introduction to Modern Bayesian Econometrics, Blackwell Publishing
Pole, A., West, M. and Harrison, P. J. (1994) Applied Bayesian Forecasting and Time Series Analysis, Chapman-Hall.
Robert, C. P. (2001). The Bayesian Choice - A Decision-Theoretic Motivation (second ed.). Springer Verlag
West, M. and Harrison, P. J. (1997). Bayesian Forecasting and Dynamic Models, Springer-Verlag.
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 assignements can yield up to 20 points over 30 and the final project can yield up to 10 points out of 30. The exam is
considered passed with the achievement of 18 total points over 30.
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
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
Cycles of seminars and lectures on the various topics
Type of exam
written and oral
2030 Agenda for Sustainable Development Goals
This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development
Last update of the programme: 15/07/2021
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