Last modified: 31 May 2022 13:05
This course offers an introduction to econometrics, which is the application of statistical techniques to provide answers to questions in finance, among others. Economic theories can predict the likely relations of financial variables, and econometrics can provide the evidence for such relations using real-world data.
As building blocks of econometrics, this course will start by covering inferential statistics, asking what inferences can be drawn about the population from a sample. You will then proceed to learn regression analysis which is the fundamental of econometrics.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Second Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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The course is structured into ten units and associated tutorials.
Unit 1: Introduction
Introduction to inferential statistics. Random sampling, criteria to choose an estimator; unbiasedness, efficiency and consistency.
Unit 2: Point estimation
Estimators with good properties: method of moments, maximum likelihood, and least squares.
Unit 3: Interval estimation
Calculations and interpretations of confidence intervals.
Unit 4: Hypothesis testing
Steps for basic hypothesis testing, p-value.
Unit 5: Regression analysis 1
Understanding simple regression model, classical linear regression model assumptions, and estimation.
Unit 6: Regression analysis 2
Extension to multiple regression model, multiple regression model assumptions, inference on population parameters.
Unit 7: Goodness-of-fit and variable specifications
R-squared, modelling linear and nonlinear relationships.
Unit 8: Issues in multiple regression analysis 1
Multicollinearity, heteroscedasticity, serial correlation.
Unit 9: Issues in multiple regression analysis 2
Omitted variables, measurement errors, simultaneity.
Unit 10: Advanced panel data methods
Pooled OLS, First Differences, Fixed Effects, Random Effects models.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 75 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
The maximum length for this coursework is five sides of A4 paper. Output files from computer software must be attached to the coursework as an appendix, which does not count as additional pages. Written feedback will be provided upon the return of the coursework. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Demonstrate basic knowledge and understanding of the assumptions and properties of the classical linear regression model, and of the effect on regression results when those assumptions are violated. |
| Procedural | Analyse | By the end of the course, students should be able to demonstrate an understanding of the purpose of econometrics and an ability to use econometrics to analyse simple economic and financial models. |
| Reflection | Create | Demonstrate an ability to formulate and evaluate testable statistical hypotheses using the regression model and econometric software and an ability to carefully interpret regression results. |
| Assessment Type | Summative | Weighting | 25 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
In-class multiple choice quizzes. Oral feeedback will be provided in the form of face-to-face teaching, explaining correct answers. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Demonstrate basic knowledge and understanding of the assumptions and properties of the classical linear regression model, and of the effect on regression results when those assumptions are violated. |
| Procedural | Analyse | By the end of the course, students should be able to demonstrate an understanding of the purpose of econometrics and an ability to use econometrics to analyse simple economic and financial models. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
Written feedback will be provided upon return of the coursework. |
|||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
|
|
||
| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Reflection | Create | Demonstrate an ability to formulate and evaluate testable statistical hypotheses using the regression model and econometric software and an ability to carefully interpret regression results. |
| Conceptual | Understand | Demonstrate basic knowledge and understanding of the assumptions and properties of the classical linear regression model, and of the effect on regression results when those assumptions are violated. |
| Procedural | Analyse | By the end of the course, students should be able to demonstrate an understanding of the purpose of econometrics and an ability to use econometrics to analyse simple economic and financial models. |
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