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

Aims and objectives

General aims for first cycle education

First-cycle courses and study programmes shall develop:

• the ability of students to make independent and critical assessments
• the ability of students to identify, formulate and solve problems autonomously, and
• the preparedness of students to deal with changes in working life.

In addition to knowledge and skills in their field of study, students shall develop the ability to:

• gather and interpret information at a scholarly level
• stay abreast of the development of knowledge, and
• communicate their knowledge to others, including those who lack specialist knowledge in the field.

(Higher Education Act, Chapter 1, Section 8)

Course objectives

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.

Main content of the course

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

Teaching methods

Lectures and computer labs.

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.

Examination methods

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 forms of examinations.

For further information, see the university's local examination regulations (in Swedish).

Grades

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 prescribe which grading system shall apply. The grade is to be determined by a teacher specifically appointed by the university (an examiner).

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

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

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

Computer Labs
Grades used are Fail (U) or Pass (G).

For further information, see the university's local examination regulations (in Swedish).

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.

Specific entry requirements

Successful completion of at least 12 credits within the course Basic Statistics, 15 credits and 1.5 credits within the course Statistics, Regression Analysis, Basic Course, 7.5 credits

For further information, see the university's admission regulations (in Swedish).

Transfer of credits for previous studies

Students who have previously completed higher education or other activities are, in accordance with the Higher Education Ordinance, entitled to have these credited towards the current programme, providing that the previous studies or activities meet certain criteria.

For further information, see the university's local credit transfer regulations (in Swedish).

Other provisions

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

Reading list and other teaching materials

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.