Syllabus for Time Series Econometrics
Tidsserieekonometri
Syllabus
- 7.5 credits
- Course code: 2ST111
- Education cycle: Second cycle
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Main field(s) of study and in-depth level:
Statistics A1N
Explanation of codes
The code indicates the education cycle and in-depth level of the course in relation to other courses within the same main field of study according to the requirements for general degrees:
First cycle
- G1N: has only upper-secondary level entry requirements
- G1F: has less than 60 credits in first-cycle course/s as entry requirements
- G1E: contains specially designed degree project for Higher Education Diploma
- G2F: has at least 60 credits in first-cycle course/s as entry requirements
- G2E: has at least 60 credits in first-cycle course/s as entry requirements, contains degree project for Bachelor of Arts/Bachelor of Science
- GXX: in-depth level of the course cannot be classified
Second cycle
- A1N: has only first-cycle course/s as entry requirements
- A1F: has second-cycle course/s as entry requirements
- A1E: contains degree project for Master of Arts/Master of Science (60 credits)
- A2E: contains degree project for Master of Arts/Master of Science (120 credits)
- AXX: in-depth level of the course cannot be classified
- Grading system: Fail (U), Pass (G), Pass with distinction (VG)
- Established: 2010-06-03
- Established by: The Department Board
- Revised: 2022-10-14
- Revised by: The Department Board
- Applies from: Autumn 2023
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Entry requirements:
120 credits including 90 credits in statistics, or 120 credits including 60 credits in statistics and 30 credits in mathematics and/or computer science.
- Responsible department: Department of Statistics
Learning outcomes
The course is an introduction to time series econometrics for second-cycle studies and treats basic themes in modern time series analysis. A student who has taken the course should:
* have a solid knowledge about basic themes in modern time series analysis
* know and be able to use concepts and notation that is frequently used in time series analysis
* know and be able to use different probabilistic results for serially dependent observations
* be familiar with different methods to estimate time series models
* be able to choose on appropriate model and estimation method for a given time series
* be able to interpret the results of an fitted model
* be aware of limitations and possible sources of errors in the analysis
Content
Difference equations. White noise, stationarity and ergodicity. Stationary ARMA processes: The Box-Jenkins approach. Prediction: Wold's theorem, test for predictive accuracy. Vector autoregressive models. Maximum likelihood estimation. Asymptotic theory for serially dependent vector autoregressive processes. Bayesian analysis. State-space model and the Kalman filter. Models of nonstationary time series: unit root and deterministic time trends. Cointegraton.
Instruction
Teaching is given in the form of lectures and tutorial classes.
Assessment
The examination takes place through a written examination at the end of the course and compulsory written assignments. The grading scales are: failed, passed and passed with distinction.
Other directives
The course is included in the Master's programme in statistics.
Reading list
Reading list
Applies from: Autumn 2023
Some titles may be available electronically through the University library.
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Hamilton, James D.
Time series analysis
Princeton, N.J.: Princeton Univ. Press, cop. 1994