- Docente: Roberto Golinelli
- Credits: 6
- SSD: SECS-P/05
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Applied Economics and Markets (cod. 5969)
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from Feb 12, 2024 to Mar 12, 2024
Learning outcomes
The aim of the course is to introduce the basic methods to empirically model the relationships among economic time-series. In doing so, a particular attention will be paid to close the gap between theoretical macro issues and the econometric practice. At the end of the course, students will be able to understand papers in this field of research, and to use/make their own empirical analyses with econometric software.
Course contents
1. Univariate analysis
1.1 Dynamic properties of time series: trend and cycle decomposition
1.2 Non stationarity and unit roots in the AR model
2. Multivatiare analysis
2.1 Spurius regression and cointegration
2.2 Dynamics of single equations in the ARDL model
2.3 Dynamics of systems in the VAR model
2.4 Dynamics and common factors
Readings/Bibliography
During the course, papers, handouts, and data will be posted in Virtuale for students regularly attending classes. During the course, in class, I will also illustrate how to use the references below.
Textbooks
- Entry level
Philip Hans Franses, Dick van Dijk and Anne Opschoor, "Time Series Models for Business and Economic Forecaststing" (2nd edition), Cambridge University Press.
or
Christiaan Heij, Paul de Boer, Philip Hans Franses, Teun Kloek and Herman K. van Dijk, "Econometric Methods with Applications in Business and Economics", Oxford University Press, part 7: Time Series and Dynamic Models.
Intermediate (main reference)
- Hashem Pesaran, "Time Series and Panel Data Econometrics", Oxford University Press, chapters 6, 12-13, 14-16, 19-22
Advanced (selected parts, in class)
- James Hamilton, "Time Series Analysis", Princeton University Press.
Teaching methods
Each lecture presents both theoretical and applied aspects of the econometric method, by using the PC screen in class to show main outcomes of the regression analysis. For this, attending regularly classes is of fundamental relevance.
Assessment methods
For students attending classes
- 30% project in which you obtain data and use them
- 70% final (written) examination
Other students
- only written examination (on the basis of Pesaran reference above)
Scale of assessment: <18: insufficient; 18-23: sufficient; 24-27:good; 28-30: very good; 30 e lode: outstanding.
Teaching tools
Methodological lectures and empirical applications with econometric software.
Links to further information
Office hours
See the website of Roberto Golinelli
SDGs


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.