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BU551V: APPLIED ECONOMETRICS (2021-2022)

Last modified: 31 May 2022 13:05


Course Overview

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.

Course Details

Study Type Postgraduate Level 5
Session Second Sub Session Credit Points 15 credits (7.5 ECTS credits)
Campus Aberdeen Sustained Study No
Co-ordinators
  • Dr Yu Aoki

What courses & programmes must have been taken before this course?

  • Any Postgraduate Programme (Studied)
  • Either MSc In International Business And Finance or Master Of Science In Financial Technology

What other courses must be taken with this course?

None.

What courses cannot be taken with this course?

None.

Are there a limited number of places available?

No

Course Description

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.


Details, including assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

Contact Teaching Time

Information on contact teaching time is available from the course guide.

Teaching Breakdown

  • 1 Computer Practical during University weeks 32 - 33
  • 1 Tutorial during University weeks 29 - 30

More Information about Week Numbers


Details, including assessment, may be subject to change until 31 July 2022 for 1st half-session courses and 23 December 2022 for 2nd half-session courses.

Summative Assessments

Problem-solving and Computer Exercises

Assessment Type Summative Weighting 75
Assessment Weeks Feedback Weeks

Look up Week Numbers

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.

Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandDemonstrate 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.
ProceduralAnalyseBy 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.
ReflectionCreateDemonstrate an ability to formulate and evaluate testable statistical hypotheses using the regression model and econometric software and an ability to carefully interpret regression results.

Class Test - Multiple Choice Questions

Assessment Type Summative Weighting 25
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

In-class multiple choice quizzes. Oral feeedback will be provided in the form of face-to-face teaching, explaining correct answers.

Learning Outcomes
Knowledge LevelThinking SkillOutcome
ConceptualUnderstandDemonstrate 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.
ProceduralAnalyseBy 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.

Formative Assessment

There are no assessments for this course.

Resit Assessments

Problem-solving and Computer Exercises

Assessment Type Summative Weighting 100
Assessment Weeks Feedback Weeks

Look up Week Numbers

Feedback

Written feedback will be provided upon return of the coursework.

Learning Outcomes
Knowledge LevelThinking SkillOutcome
Sorry, we don't have this information available just now. Please check the course guide on MyAberdeen or with the Course Coordinator

Course Learning Outcomes

Knowledge LevelThinking SkillOutcome
ReflectionCreateDemonstrate an ability to formulate and evaluate testable statistical hypotheses using the regression model and econometric software and an ability to carefully interpret regression results.
ConceptualUnderstandDemonstrate 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.
ProceduralAnalyseBy 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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