| Learning objectives |
- Understand the theoretical foundations and assumptions behind
time series models.
- Interpret and communicate the results obtained from time series
models, including parameter estimates, significance tests, and
forecast intervals (see Nordic Nine #6: You are critical when
thinking and constructive when collaborating).
- Clearly articulate the implications of the analysis for
decision-making in business, economics, or finance.
- Demonstrate the ability to preprocess time series data by
handling missing values, outliers, and irregularities
- Apply techniques for data cleaning and normalization to ensure
the quality of the dataset
- Utilize R programming language for various tasks in time series
analysis, including data manipulation, model fitting, and
visualization (see Nordic Nine #2: You are analytical with data and
curious about ambiguity).
- Work on practical projects that involve applying time series
analysis techniques to real-world economic, business, or financial
datasets (see Nordic Nine #4: You are competitive in business and
compassionate in society).
- Identify common challenges in time series analysis, such as
seasonality, non-stationarity, and autocorrelation
- Discuss techniques for addressing these challenges, including
differencing, detrending, and model selection. (see Nordic Nice #3:
You recognize humanity’s challenges and have the entrepreneurial
knowledge to help resolve them)
- Generate informative visualizations, such as time plots,
seasonal decomposition plots, and autocorrelation plots
- Interpret visualizations to extract meaningful patterns and
trends in the time series data.
|
| Course prerequisites |
| Introduction to statistics and basic econometrics
(with regression). |
| Examination |
|
Time Series
for Economics, Business and Finance:
|
| Exam
ECTS |
7,5 |
| Examination form |
Oral exam based on written product
In order to participate in the oral exam, the written product
must be handed in before the oral exam; by the set deadline. The
grade is based on an overall assessment of the written product and
the individual oral performance, see also the rules about
examination forms in the programme regulations. |
| Individual or group exam |
Oral group exam based on written group
product |
| Number of people in the group |
2-4 |
| Size of written product |
Max. 10 pages |
|
Definition of number of pages:
Groups of
2 students 5 pages max.
3-4 students 10 pages max. |
| Assignment type |
Synopsis |
| Release of assignment |
Subject chosen by students themselves, see
guidelines if any |
| Duration |
Written product to be submitted on specified date and
time.
10 min. per student, including examiners' discussion of grade,
and informing plus explaining the grade |
| Grading scale |
7-point grading scale |
| Examiner(s) |
Internal examiner and second internal
examiner |
| Exam period |
Winter |
| Make-up exam/re-exam |
Same examination form as the ordinary exam
Re-take exam is to be based on the
same report as the ordinary exam:
*if a student is absent from the oral exam due to documented
illness but has handed in the written group product she/he does not
have to submit a new product for the re-take.
*if a whole group fails the oral exam they must hand in a revised
product for the re-take.
*if one student in the group fails the oral exam the course
coordinator chooses whether the student willhave the oral exam on
the basis of the same product or if he/she has to hand in a revised
product for there- take.
|
Description of the exam
procedure
Working in groups of 2-4 you are supposed to conduct a short
empirical project with time series data of your own choice. In
summary, you need to (i) find the data, (ii) apply and evaluate at
least two different models for the data from the course's
syllabus, (iii) demonstrate understanding of the methods you chose
to apply, (iv) justify the choices you have made regarding the data
and the methods, (v) interpret the results appropriately (see
Nordic Nine #6: You are critical when thinking and constructive
when collaborating).
The theme(s) of the synopsis must be prepared by the student(s).
The oral examination will be based on the synopsis. The examiner
may ask questions within the framework of the entire syllabus.
The synopsis is written in parallel with the elective. The
synopsis must be submitted two weeks before the date of the
exam.
|
|
| Course content, structure and pedagogical
approach |
|
Upon successfully finishing this course, students will acquire
the expertise to meticulously clean, visually represent, and
analyze time series data crucial in the realms of business,
economics, and finance. This journey of learning encompasses
mastering various data collection methods, including adeptly
extracting information from online sources. Emphasizing the widely
utilized R software, prevalent in financial institutions, firms,
and academia, students will become adept at data
manipulation—empowering them with a coveted skill set.
The curriculum delves into time series methodologies, enabling
students to construct models and make insightful forecasts. Armed
with these skills, students will find themselves well-equipped for
roles in analytics departments across industries or poised for
advanced academic pursuits in fields such as economics, finance,
marketing, and related disciplines (see Nordic Nice #3: You
recognize humanity’s challenges and have the entrepreneurial
knowledge to help resolve them). This course serves as a
springboard, propelling students toward a future where they can
harness the power of data to drive informed decision-making in
diverse professional settings (see Nordic Nine #4: You are
competitive in business and compassionate in society).
Topics:
- Autoregressive Integrated Moving Average (ARIMA)
model.
- Seasonal Autoregressive Integrated Moving Average
(SARIMA) model.
- Modelling structural breaks.
- Autoregressive Distributed Lag (ADL) model.
- Generalized Autoregressive Conditional Heteroscedasticity
(GARCH) model.
- Vector Autoregressive (VAR) model.
- Structural Vector Autoregressive (SVAR) model.
- Error Correction Model (ECM).
|
| Description of the teaching methods |
| The course is a mix of lectures and
computer-based, hands-on examples of how to use R for time series
data analysis |
| Feedback during the teaching period |
| Feedback is available on a continuous basis
throughout the semester. |
| Student workload |
| Classes |
30 hours |
| Exam / preparation |
176 hours |
|
| Expected literature |
|
Applied Econometric Time Series - Walter Enders, 4th Edition,
Wiley
|
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