AØKK08096U The Cointegrated VAR Approach: Methodology and Applications
The course includes the topics:
- Introduction to central concepts: vector autoregressive processes, error-correction models, non-stationary processes and cointegration.
- Representation of cointegrated processes.
- Estimation and testing in the cointegrated VAR model.
- Identification and estimation of structural econometric models and common trends models.
- Introduction to processes integrated of order 2.
The aim of this course is to provide the students with a profound theoretical and practical knowledge of the econometric analysis of non-stationary time-series using multivariate dynamic models. At the end of the course students should be able to perform cointegration analyses based on a given set of data and to critically assess empirical analyses of macroeconomic time series. The overall goal is that the students - after having completed the course - should be able to: - Formulate a vector autoregressive (VAR) model for a given set of data and test whether it is a congruent representation of the information in the data. - Formulate the hypotheses of unit roots and cointegration as restrictions on the VAR model. Test for the cointegration rank of the VAR model. - Explain the role of constants, trend terms, and dummy variables in the cointegrated VAR model. - Estimate the parameters of the cointegrated VAR model using maximum likelihood. Interpret the results in terms of equilibrium relationships and driving common trends. - Formulate and test hypotheses on the cointegrating relationships and the equilibrium adjustments. - Analyze whether the VAR model has constant parameters. - Explain when a structure is exact-, under- or overidentified. - Impose identifying restrictions on the long-run and short-run structure of the model - Impose identifying restrictions on the common trends of the model and perform a structural VAR analysis. - Understand the basics of the cointegration model for variables integrated of order two and perform a nominal-to-real transformation. - Apply the cointegration theory to a real problem and interpret the results at the background of available theories.
After completing this course it will NOT be possible to take the course Advanced Macroeconometrics since the content of these two courses are similar.
- Category
- Hours
- Lectures
- 60
- Preparation
- 146
- Total
- 206
- Credit
- 10 ECTS
- Type of assessment
- OtherPassing the course requires a successful completion of the empirical project that is chosen and approved by the teachers at the start of the course. The grade is based on the quality relative to the difficulty of the analysis. A more demanding analysis will get a higher grade than a less demanding even when the former is less perfect than the latter. Finally it needs to be emphasized that the student should be prepared to work extremely hard during the three weeks of the summer school (and before) as it is otherwise hard to pass the exam (unless the student is mathematically very talented).
- Aid
- All aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- External censorship
100 % censorship
- Exam period
- Will be updated before the start of the semester
- Re-exam
- Same as ordinary. But if only a few students have registered for the re-exam, the exam might change to an oral exams with a synopsis to be handed in. This means that the examination date also will change.
Criteria for exam assesment
Course information
- Language
- English
- Course code
- AØKK08096U
- Credit
- 10 ECTS
- Level
- Full Degree Master
- Duration
- 1 block
- Placement
- Summer
- Schedule
- August 4 - August 24
- Course capacity
- 30
- Continuing and further education
- Price
- For price, please click here
- Study board
- Department of Economics, Study Council
Contracting department
- Department of Economics
Course responsibles
- Katarina Juselius (17-71677a67786f746734707b796b726f7b79466b69757434717b346a71)
- Søren Johansen (14-7672756871316d726b64717668714368667271316e7831676e)
- Morten Nyboe Tabor (18-7476797b6c7535758069766c357b68697679476c6a767535727c356b72)
Lecturers
Valeria Zhavoronkina