DRE 2012 Dynamic Marketing Models
DRE 2012 Dynamic Marketing Models
This course is designed to enable students to design, estimate and communicate results of time series models in marketing. Special attention is devoted to dynamic systems of equations such as Vector Autoregressive and Vector error Correction models.
By the end of this course, the PhD candidate will know the trade-offs in selecting and implementing time series models. Acquired knowledge includes the specific steps in the research process, communicating these steps in the publication process, and formulating answers to philosophy-of-science type questions on e.g. the Lucas Critique.
By the end of the course, the PhD candidate will have a strong basis in 3 skills:
1) How to design a dynamic marketing model from a real-life marketing problem
2) How to specify and estimate the appropriate model in Eviews software
3) How to report on the findings in oral (presentation) and written (paper) format
- Proper reading and understanding of dynamic models in top marketing journals
- Translating conceptual frameworks and hypotheses into a mathematical model
- Presenting and discussing own work in front of peers
- Addressing marketing problems with dynamic system models
- Specifying and estimating dynamic system models
- What-if and forecasting analysis in dynamic systems
- Interpreting results in dynamic system models
- Communicating and publishing papers on dynamic marketing problems
- A detailed schedule with specific course topics will be distributed during the first meeting.
The class will meet each day for a week. The class will employ a combination of lectures and discussions of assigned readings, as well as exercises to be completed outside of class. Students are expected to thoroughly read the required readings or complete the exercises prior to each meeting.
If students have to miss class on a particular day, it is the students’ responsibility to get notes from a classmate. Let the course responsible know as soon as possible if you have to miss a class.
If students experience any problem(s) with the seminar or any of the classmates it is expected that the students report any problem(s) that they are not able to resolve themselves to course responsible as soon as possible.
Unexcused absence will result in a lower class participation grade
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Enrollment in a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
It is assumed that every student is familiar with the general principles of research design, measurement, and multivariate statistical analysis. While this course does not require prior knowledge of matrix algebra, references to such topics will be provided.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 60 Grouping: Individual Duration: 1 Month(s) Comment: 4 assignments. Exam code: DRE20121 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Activity Form of assessment: Presentation Weight: 15 Grouping: Individual Comment: Oral presentation of assignment 3. Exam code: DRE20121 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Activity Form of assessment: Class participation Weight: 25 Grouping: Individual Exam code: DRE20121 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Student's own work with learning resources | 140 Hour(s) | |
Teaching | 30 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.