Run by Bangor Business School
20.000 Credits or 10.000 ECTS Credits
Organiser: Prof John Ashton
Overall aims and purpose
The course introduces students to regression analysis, the pitfalls that empirical researchers confront when using these tools, and the methods they use to overcome them. Throughout there is a strong focus on trying to establish cause and effect, rather than just associations.
The course will cover the linear regression model (LRM), ordinary least squares, goodness of fit and the explanatory power of a regression model, hypothesis testing, model diagnostics, Heteroskedasticity, Multicollinearity, Dummy Variables, Autocorrelation, measurement error, simultaneity bias, endogeneity, Instrumental variables, Maximum Likelihood Estimation, Panel data (Static and Dynamic Models).
B- to B+ (60-69%): Very good performance Most of the relevant information accurately deployed. Good grasp of theoretical/conceptual/practical elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Evidence of the use of creative and reflective skills.
D- to D+ (40-49%): No major omissions or inaccuracies in the deployment of information/skills. Some grasp of theoretical/conceptual/practical elements. Integration of theory/practice/information present intermittently in pursuit of the assessed work's objectives.
A- to A+ (70%+): Outstanding Performance. The relevant information accurately deployed. Excellent grasp of theoretical/conceptual/practice elements. Good integration of theory/practice/information in pursuit of the assessed work's objectives. Strong evidence of the use of creative and reflective skills.
C- to C+
C- to C+ (50-59%): Much of the relevant information and skills mostly accurately deployed. Adequate grasp of theoretical/conceptual/practical elements. Fair integration of theory/practice/information in the pursuit of the assessed work's objectives. Some evidence of the use of creative and reflective skills.
Appropriately apply econometric techniques in the assessment of financial and banking issues.
Demonstrate team work skills in analysing a contemporary econometric issue.
Collect and critically evaluate data from primary and secondary resources.
Demonstrate a critical awareness of the merits and pitfalls of the implicit assumptions and the limitations underlying empirical models.
Demonstrate the ability to assess and interpret surveys, reports and academic literature that apply advanced econometrics.
Demonstrate a thorough awareness of theoretical and practical aspects of econometrics.
Teaching and Learning Strategy
Lectures: 3 hours of lectures each week (10 weeks x3 hours).
|Practical classes and workshops||
Computer workshops: 1 hour each week.
Review and reflect upon the course material.
- Numeracy - Proficiency in using numbers at appropriate levels of accuracy
- Computer Literacy - Proficiency in using a varied range of computer software
- Critical analysis & Problem Solving - Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
- Presentation - Able to clearly present information and explanations to an audience. Through the written or oral mode of communication accurately and concisely.
- Teamwork - Able to constructively cooperate with others on a common task, and/or be part of a day-to-day working team
- Self-awareness & Reflectivity - Having an awareness of your own strengths, weaknesses, aims and objectives. Able to regularly review, evaluate and reflect upon the performance of yourself and others
Subject specific skills
- Abstraction. From the study of economic principles and models, students see how one can abstract the essential features of complex systems and provide a useable framework for evaluation and assessment of the effects of policy or other exogenous events. Through this, the typical student will acquire proficiency in how to simplify while still retaining relevance. This is an approach that they can then apply in other contexts, thereby becoming more effective problem-solvers and decision-makers.
- Problem solving and critical analysis: analysing facts and circumstances to determine the cause of a problem and identifying and selecting appropriate solutions.
- Research: the ability to analyse and evaluate a range of business data, sources of information and appropriate methodologies, which includes the need for strong digital literacy, and to use that research for evidence-based decision-making.
- Numeracy: the use of quantitative skills to manipulate data, evaluate, estimate and model business problems, functions and phenomena.
Talis Reading listhttp://readinglists.bangor.ac.uk/modules/etb-3317.html
Stock and Watson Introduction to Econometrics: International Edition.
C.Dougherty. Introductory to Econometrics