Statistics, Time Series Analysis and Forecasting, Intermediate Course, 7.5 credits

Learning outcomes

After completion of the course, the student will have

• knowledge of basic concepts in time series analysis
• knowledge of time series regression
• knowledge of ARIMA modelling of stationary and nonstationary time series
• knowledge of frequently used volatility models
• an understanding of problems arising when analyzing unit root processes
• the abilty to apply the knowledge on real world time series and forecast problems
• the ability to critically review and evaluate time series models and choose the best modelling approach
• an understanding of the use of time series models for forecasting and the limitations of the methods
• the ability to convey relevant aspects of modelling issues and results, for example in the role of statistical consultant
• a good foundation for further studies and the ability to take in new developments in the field.

Content

Basic concepts in time series analysis: stationarity, autocovariance, autocorrelation, partial autocorrelation.

ARIMA modelling: Autoregressive models, moving average models, duality, model properties, parameter estimates, forecasts.

Volatility models: ARCH and GARCH modelling, testing strategy for heteroscedastic models, volatility forecasts.

Integrated processes: Difference stationarity, teting for unit roots, spurious correlation

Multivariate time series: Time series regression, VAR models, cointegration, forecasting properties

Examinations and grades

Written Examination, 6 credits (Code: A001)
Grades used are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Computer Labs, 1.5 credits (Code: A002)
Grades used are Fail (U) or Pass (G).

According to the Higher Education Ordinance, Chapter 6, Section 18, a grade is to be awarded on the completion of a course, unless otherwise prescribed by the university. The university may determine which grading system is to be used. The grade must be determined by a teacher specifically nominated by the university (the examiner).

In accordance with university regulations on grading systems for first and second-cycle courses and study programmes (Vice-Chancellor’s decision ORU 2018/00929), one of the following grades is to be used: fail (U), pass (G) or pass with distinction (VG). For courses included in an international master’s programme (60 or 120 credits) or offered to the university’s incoming exchange students, the A to F grading scale is to be used. The vice-chancellor, or a person appointed by them, may decide on exceptions from this provision for a specific course, if there are special grounds for doing so.

The grades used on this course are Fail (F), Sufficient (E), Satisfactory (D), Good (C), Very Good (B) or Excellent (A).

Comments on grades

To obtain a passing grade for the course as a whole, a passing grade is required on all course components. The final grade for the entire course is a function of the grades of the course components. Detailed information on the requirements for different grade levels is given at the course start.

Modes of assessment

Written Examination, 6 credits (Code: A001)

Computer Labs, 1.5 credits (Code: A002)

For students with a documented disability, the university may approve applications for adapted or other modes of assessment.

For further information, see the university's local examination regulations.

Specific entry requirements

Successful completion of at least 12 credits within the course Basic Statistics, 15 credits and 3 credits within the course Data mining and business analytics, 15 credits or
Successful completion of at least 12 credits within the course Basic Statistics, 15 credits and 1.5 credits within the course Regression Analysis, 7.5 credits.

For further information, see the university's admission regulations.

Other provisions

Teaching language is English provided that at least one student does not speak Swedish. Otherwise teaching language may be Swedish.

Students who have been admitted to and registered on a course have the right to receive tuition and/or supervision for the duration of the time period specified for the particular course to which they were accepted (see, the university's admission regulations (in Swedish)). After that, the right to receive tuition and/or supervision expires.

Reading list and other learning resources

Required Reading

Becketti, Sean (2013)
Introduction to Time Series Using Stata
Stata Press, College Station, Texas, ISBN/ISSN: 978-1-59718-132-7, 443 pages. Chapters included: 3-10.