GRA 4159 Trends, Cycles, and Signal Extraction from a Macroeconomic Perspective
GRA 4159 Trends, Cycles, and Signal Extraction from a Macroeconomic Perspective
No business decision can be made in isolation from general market developments. In this course you will learn about important economic relationships, ranging from macroeconomic accounting identities to simple models describing the behavioral relationships giving rise to these accounting identities. We will study short- and long-run economic fluctuations and discuss the role of productivity and monetary and fiscal policy, both from a theoretical and empirical perspective.
In terms of methodology you will be given a thorough introduction to time series analysis, trend and cycle decompositions, the usage of state-space and factor models, and the celebrated Kalman Filter. These tools are used heavily not only in applied macroeconomic analysis, but also in all other domains where time series analysis is important.
By the end of the course, the student:
- Can describe the most widely used national account statistics and accounting principles as well as their relationship with other important macroeconomic indicators such as inflation and interest rates.
- Can explain business cycle fluctuations using modern macroeconomic theory, and demonstrate how monetary and fiscal policy interventions affect the economy in these models.
- Is able to discuss and differentiate different factors determining long-term economic growth.
- Can explain, compare and choose among different time series methods to analyze both short- and long-run economic fluctuations from an empirical perspective.
By the end of the course, the student:
- Can choose among and apply relevant economic data and indicators.
- Can create empirical models to analyze macroeconomic trends and fluctuations and make and evaluate out-of-sample predictions.
- Can apply different time series filters, estimate latent variables, and analyze large time series data sets using dimension reduction techniques.
By the end of the course, the student:
- Has gotten a broad understanding of important factors determining business cycle fluctuations and long-run growth, and how to analyze this both from a theoretical and empirical perspective.
- Is able to describe and apply fundamental time series tools, and extrapolate their time series knowledge and skills obtained in this course to other relevant fields.
- Macroeconomic data and the National Account Statistics
- The most important accounting identities
- Inflation, interest rates, etc.
- Business cycles, economic growth, and economic policy
- Sources of business cycle fluctuations and growth
- The Sveen and Røisland (2018) model
- Time series processes and trend and cycle decompositions
- Univariate and multivariate time series processes
- Seasonality
- Trend and cycle decompositions
- Signal extraction and dimension reduction
- Dimension reduction
- State-space models and factor models
- Kalman Filtering
- Forecasting
Lectures and practical exercises that must be solved on the computer. The learning activities will combine 2/3 lectures, 1/3 case discussions and videos. Students are expected to prepare to the lectures by reading assigned materials and participate actively in the discussion of the lecture topics. Software: Python/R
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
At resit, all exam components must, as a main rule, be retaken during next scheduled course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Students are expected to have completed all prior courses in the program.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Individual Duration: 1 Semester(s) Comment: Written report consisting of 3-4 assignments given throughout the semester. Requires Python/R. Students will be given the opportunity to present and get feedback on their work during the semester and before submitting the report. Students are encouraged to support each other, but the assignments must be solved individually. All assignments must be passed to pass the exam. All exams must be passed to obtain a final grade in the course. Exam code: GRA 41591 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Invigilation Weight: 60 Grouping: Individual Support materials:
Duration: 3 Hour(s) Comment: - Exam code: GRA 41592 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Seminar groups | 12 Hour(s) | |
Student's own work with learning resources | 80 Hour(s) | |
Submission(s) | 32 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.