- 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. 6756)
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 Non stationarity and unit roots in the AR model
1.2 Trend-cycle decomposition: stationary cicles with HP filter
2. Multivatiare analysis
2.1 Stationary dynamic systems modelling with VAR model
2.2 Granger causality and IRF (in the Cholesky SVAR model)
3. Single-equation models
3.1 Spurius regression and cointegration
3.2 ARDL model and ECM/Bewley representations
4. Hybrid macroeconometric models
4.1 Johansen cointegration in the VAR model
4.2 Cointegration rank and long-run identification
4.3 Alternative identified structures
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.
It is important to keep in mind that 'attending classes' means keeping up with the course by studying and doing the exercises every week, not just showing up in the classroom.
Finally, please note that the topics covered in the refresher courses in mathematics and statistics, as well as the statistics and econometrics courses from the last semester, are all considered known and essential prerequisites.
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
Scale of assessment: <18: insufficient; 18-23: sufficient; 24-27:good; 28-30: very good; 30 e lode: outstanding.
Note for Erasmus (International) Students:
Since this course is a module of the integrated course "Macroeconomic Analysis," selecting only this module and not the twin module "Macroeconomic Models for Policy Analysis" may result in problems and extra work due to the integration of the exams for both modules.
Teaching tools
The full module teaching consists of 10 methodological lectures (30 hours) and 4 online extra meetings for empirical applications with Stata and Gretl econometric software (11 hours).
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.