THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2024/2025

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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Industrial Organisation (CMSE11450)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThe course will provide students the foundations of econometric analysis using the industry and firm level data. In the era of big data, the importance of industrial analytic skills for graduates in business can hardly be overstated.
Course description Industry analysis has a long history which can be traced back from Michael Porter's five-force analysis. Nowadays, this practice is widely adopted in a lot of business activities including business consultancy, strategic analysis, etc. This course aims at training students in the field of industrial analytics using a variety of methodologies. To be more specific, this course covers the typical theories of industrial organisation along with a range of techniques to analyse an industry, assess business models, identify competition patterns, and propose appropriate strategies for high-level managers.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements For Business School PG students only, or by special permission of the School. Please contact the course secretary.
Course Delivery Information
Academic year 2024/25, Not available to visiting students (SS1) Quota:  None
Course Start Block 4 (Sem 2)
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 6, Seminar/Tutorial Hours 6, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 86 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework (individual) - assesses all course Learning Outcomes
Feedback Formative: Feedback will be provided throughout the course.

Summative: Feedback will be provided on the assessment within agreed deadlines.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand how to use data analytical techniques help decision making in a business environment.
  2. Describe how firms differentiate with each other: vertical & horizontal differentiation. understand common tools for analysing these models empirically.
  3. Identify the effectiveness of firms' strategies.
  4. Have an overview of structural estimation.
Reading List
Recommended Textbook:
Angrist, J. D., & Pischke, J. S. (2008). Mostly harmless econometrics: An empiricist's companion. Princeton university press.
Aguirregabiria, V. (2012). Empirical industrial organization: models, methods, and applications. University of Toronto, Preliminary version.

Recommended Readings:
Nevo, A. (2000). A practitioner's guide to estimation of random-coefficients logit models of demand. Journal of economics & management strategy, 9(4), 513-548.
Lynne Pepall, Dan Richards and George Norman, "Industrial Organization: Contemporary Theory and Empirical Applications". 5th Edition, 2013, ISBN-13: 978-1-118-25030-3
Train, K. E. (2009). Discrete choice methods with simulation. Cambridge university press.
Don E.Waldman, Elizabeth J. Jensen (2013), "Industrial Organization: Theory and Practice", ISBN-13: 978-1292039985
Lynne Pepall, Dan Richards and George Norman, "Industrial Organization: Contemporary Theory and Empirical Applications". 5th Edition, 2013, ISBN-13: 978-1-118-25030-3
Oz Shy (1996), "Industrial Organization: Theory and Application", ISBN-13: 978-0262691796
Geoff Harcourt, Clive W. J. Granger (1999), "Empirical Modeling in Economics: Specification and Evaluation", ISBN-13: 978-0521778251
Additional Information
Graduate Attributes and Skills Research & Enquiry:
On completion of the course, students should be able to:
-Understand how to use data analytical techniques help decision making in a business environment
-Describe how firms differentiate with each other: vertical & horizontal differentiation. understand common tools for analysing these models empirically
-Identify the effectiveness of firms' strategies

Personal & Intellectual Autonomy:
On completion of the course, students should be able to:
-Use empirical analysis for better decision making
-Discuss advantages and drawbacks of popular analytic techniques
-Use state-of-the-art tools in conducting industrial analysis
-Develop appropriate programming skills for industrial analysis

Communication skills:
On completion of the course, students should be able to:
-Explain the implications of formal/quantitative models to general audiences
-Use formal/quantitative models to elaborate strategic concerns in managerial and economic problems
KeywordsNot entered
Contacts
Course organiserDr Mustapha Douch
Tel:
Email: Mustapha.Douch@ed.ac.uk
Course secretaryMiss Quinny Jiang
Tel:
Email: Quinny.Jiang@ed.ac.uk
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