Undergraduate Course: Essentials of Econometrics (ECNM10052)
|School||School of Economics
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 10 (Year 3 Undergraduate)
||Availability||Available to all students
|Summary||Essentials of Econometrics provides an introduction to econometric theory and practice for advanced undergraduate students who have completed courses in probability and statistics, microeconomics and macroeconomics. EE aims to ensure that all economics honours students have a sound grasp of the basic techniques of modern empirical economics.
Essentials of Econometrics (EE) provides an opportunity to learn skills that are important for later stages of the Economics programme, and many future career and life contexts. EE aims to ensure that all economics honours students have a sound grasp of the basic techniques of modern empirical economics.
The topics covered are likely to include: statistics (review of probability distributions, statistical inference, estimation and hypothesis testing); the linear regression model (two-variable model, multiple regression, functional forms, dummy variables); regression analysis in practice (model selection criteria and tests, multicollinearity, heteroskedasticity, autocorrelation).
EE includes weekly lab sessions to reinforce lectures, with exercises which foster 'learning-by-doing'. The course provides an opportunity to develop and practice key practical skills in computing, data gathering, processing, analysis and presentation.
Information for Visiting Students
|Pre-requisites||Visiting students must have an equivalent of at least 4 semester-long Economics courses at grade B or above for entry to this course. This MUST INCLUDE courses in Intermediate Macroeconomics (with calculus); Intermediate Microeconomics (with calculus); and Probability and Statistics. If macroeconomics and microeconomics courses are not calculus-based, then, in addition, Calculus (or Mathematics for Economics) is required.
|High Demand Course?
Course Delivery Information
|Academic year 2022/23, Available to all students (SV1)
|Learning and Teaching activities (Further Info)
Lecture Hours 20,
Seminar/Tutorial Hours 13.5,
Supervised Practical/Workshop/Studio Hours 13.5,
Summative Assessment Hours 3.5,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Project (25%) due 13 November 2020
2 hour degree exam in December (75%)
The degree examination must be passed in order to pass the course.
||The first part of the lecture of week 10 will be used to provide general feedback on the project reports. Students will also be able to obtain further individual or group feedback after the assessment from any member of the Course Team.
||Hours & Minutes
|Main Exam Diet S1 (December)||2:00|
On completion of this course, the student will be able to:
- A knowledge and understanding of key econometric techniques for the empirical analysis of economic phenomena, along with application of these techniques in a variety of contexts.
- Research and investigative skills such as problem framing and solving and the ability to assemble and evaluate complex evidence and arguments.
- Communication skills in order to critique, create and communicate understanding and to collaborate with and relate to others.
- Personal effectiveness through task-management, time-management, teamwork and group interaction, dealing with uncertainty and adapting to new situations, personal and intellectual autonomy through independent learning.
- Practical/technical skills such as, modelling skills (abstraction, logic, succinctness), qualitative and quantitative analysis and interpretation of data, programming of statistical packages and general IT literacy.
|J. H. Stock and M. W. Watson, Introduction to Econometrics, (3rd revised edition) ISBN 9781292071312|
|Graduate Attributes and Skills
||See Learning Outcomes
|Additional Class Delivery Information
||Students are ALSO expected to attend weekly tutorials (start in week 2) and computer lab sessions (start in week 2).
|Course organiser||Prof Maia Guell
Tel: (0131 6)50 8351
|Course secretary||Miss Lisa Jones
Tel: (0131 6)51 5958