AØKK08204U Adv. Macroeconometrics

Volume 2014/2015
Education
MSc in Economics
Content

The focus of this course is on likelihood based analysis of the cointegrated VAR model with an emphasis on applicability, particularly in the field of macroeconomics and international finance. Cointegration analysis is a means to uncover, estimate and test stationary relations among non-stationary variables. The reason why this is interesting is that such stationary relations often can be interpreted as equilibrium relations between economic variables. Within the cointegrated VAR model it is possible to investigate dynamic interaction and feed-back effects, in particular how deviations from a steady-state relation affect the economic system. Furthermore, it is also possible to make inference on the common driving trends which have generated the non-stationarity of the data. The reason why this is interesting is that these common trends can be interpreted in terms of unanticipated shocks to the variables of the system. In short the cointegrated VAR model allows us to investigate the economic reality as a system of pulling forces (the equilibrium correction forces) and the pushing forces (the common stochastic trends). The course includes the topics:
(i) Introduction to central concepts: vector autoregressive processes, error-correction models, non-stationary processes and cointegration. (ii) Representation of cointegrated processes. (iii) Estimation and testing in the cointegrated VAR model. (iv) Identification and estimation of structural econometric models and common trends models. (iv) Introduction to processes integrated of order 2.

Learning Outcome

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 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 theory to perform and interpret an empirical cointegration analysis.

Main textbooks:
[1] Juselius, K. (2007): The Cointegrated VAR Model: Methodology and Applications, Oxford University Press.

Additional Material:
[2] Johansen, S. (1996): Likelihood Based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press.

Students should know the principle of maximum likelihood estimation and understand the dynamic linear model corresponding to Quantitative Methods 3. In addition, a fairly good knowledge of macroeconomics at least corresponding to the basic course in macroeconomics is recommended.
2 hours of lectures per week and 2 lectures each second week for 14 weeks.
2 hours of class for 14 weeks.
  • Category
  • Hours
  • Class Instruction
  • 28
  • Lectures
  • 42
  • Preparation
  • 161
  • Total
  • 231
Credit
7,5 ECTS
Type of assessment
Written assignment
The final assessment is a 48 hours individual take-home exam. The exam paper may be written in Danish or English.
Marking scale
7-point grading scale
Censorship form
External censorship
Exam period
Will be updated during the semester
Re-exam
Oral with the non-passed take-home assessment handed in again.
Criteria for exam assesment

The Student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.