ECON4165 – Applied time series analysis: econometrics and machine learning
Schedule, syllabus and examination date
Course content
The last decade has brought about an increase in the availability of new types of data, which are often messier and larger than what economists are used to. This requires a broader and more varied toolbox than what economists in both academia, policy making, and consulting are used to. This includes the need for economists to obtain hands-on experience working with real-world data and knowledge of what models to use in what situations.
The first part of the course will introduce a range of well-known models from time series econometrics. This will be done all the while students continuously work with real world data. In the last part of the course, students will be exposed to machine learning models, and how to use them in a time series context. Throughout the course, the student will be taught how to predict time series data both in economics, finance and business.
The focus of the course will be applied, rather than theoretical, but theoretical foundations of all models will be presented in the beginning of lectures.
Learning outcome
Knowledge
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Correctly download, clean, and organise time series data from a variety of online databases and sources?
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Knowledge of the most common time-series econometrics models and when to use them?
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Widening of the toolbox, and how to use econometrics and machine learning models in a real-world context
Skills
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Write a program in R to undertake analysis of data or build models
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Import data from various sources and in different formats and transform them into an analysable format?
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Implement time-series models
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Implement machine learning models
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Using the models above to predict time series relevant for business, economics, and finance
Competence
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An understanding on what types of problems in time-series that can be solved using econometric and machine learning models?
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A clear understanding on the limits of models?
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A concept of how to evaluate model both quantitatively and qualitatively
Admission to the course
Students admitted to study programmes at UiO must each semester register?which courses and exams they wish to sign up for in Studentweb.
Students not admitted to the Master’s programme in Economics or the Master’s programme in Economic Theory and Econometrics (Samfunns?konomisk analyse),?can apply for admission to one of our study programmes, or apply for?guest student status.
Formal prerequisite knowledge
ECON4150 – Introductory Econometrics – Universitetet i Oslo (uio.no)
Teaching
Lectures and seminars.?
You must bring your own laptop to be able to attend the teaching and seminars.?
Examination
Term paper. The students will be given the responsibility to define and answer an individual research question. Instructions will be given in class.?
A term paper or equivalent that is passed may not be resubmitted in revised form.
If you?withdraw from the exam?after the deadline, this will be counted as an examination attempt.
Examination support material
All exam support materials are allowed during this exam. Generating all or part of the exam answer using AI tools such as Chat GPT or similar is not allowed.
Language of examination
The examination text is given in English. You may submit your response in Norwegian, Swedish, Danish or English.
Grading scale
Grades are awarded on a scale from A to F, where A is the best grade and F?is a fail. Read more about?the grading system.
Resit an examination
If you are sick or have another valid reason for not attending the regular exam, we offer a?postponed exam?later in the same semester.
There are restrictions on resitting this exam. See further information about?resitting an exam.
More about examinations at UiO
- Use of sources and citations
- Special exam arrangements due to individual needs
- Withdrawal from an exam
- Illness at exams / postponed exams
- Explanation of grades and appeals
- Resitting an exam
- Cheating/attempted cheating
You will find further guides and resources at the web page on examinations at UiO.