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Module Specifications..

Current Academic Year 2024 - 2025

Please note that this information is subject to change.

Module Title
Module Code
School
Module Co-ordinatorSemester 1: Ann Largey
Semester 2: Ann Largey
Autumn: Ann Largey
Module TeachersAnn Largey
NFQ level 8 Credit Rating
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
None
Description

This module builds on the quantitative stream of the BSIF programme, in particular on Statistics and Data Analysis, to present a more in-depth approach to Econometrics and provide a basis for more advanced theory and techniques encountered by students in the final semester. You will learn how to deal with problems that arise in practice when working with regression models, eg choice of functional form and interpretation of results. The course emphasises the fundamental conceptual and theoretical foundations of Econometrics and gives an opportunity to apply the theory practically using software commonly employed by Economics and Finance specialists.

Learning Outcomes

1. Define and apply concepts in Probability Theory, Hypothesis Testing and Inference in a regression context.
2. Explain the Gauss Markov Theorem and the theoretical assumptions required to produce BLUE estimators in OLS
3. Explain the underpinnings of MLE and under what conditions MLE and OLS produce equivalent estimates.
4. Identify and solve basic practical problems arising in applied regression analysis
5. Plan an applied econometrics research project to be conducted using financial, economics or other social science based data



Workload Full-time hours per semester
Type Hours Description
Lecture363 hours per week
Tutorial9Weekly tutorial/practical starting in week 3 of term
Group work30Research paper proposal, including meetings with lecturer for discussion
Independent Study50Preparation for lectures, tutorials, tests.
Total Workload: 125

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Preliminaries
Probability theory Common Probability Distributions and Hypothesis testing Rules on Expectations, Variance, Covariance

Review of OLS
i) Review of Single variable regression using OLS estimation ii) Multivariate regression

Gauss-Markov Theorem and BLUE estimates
What is meant by BLUE? Prove Gauss Markov Theorem.

Maximum Likelihood Estimation
Maximum Likelihood Estimation - underpinnings and comparison with OLS

Practical issues
Introduction to general problems in practical work; Relaxation of Gauss Markov Assumptions; Solutions and corrections where possible. (i) Functional form (ii) Omitted variables (iii) Multicollinearity (iv) Heteroscedasticity

Assessment Breakdown
Continuous Assessment% Examination Weight%
Course Work Breakdown
TypeDescription% of totalAssessment Date
In Class Testn/a25%Week 6
Group project Research project proposal and collection of data.25%Week 12
In Class TestEnd of semester exam50%Sem 1 End
Reassessment Requirement Type
Resit arrangements are explained by the following categories;
1 = A resit is available for all components of the module
2 = No resit is available for 100% continuous assessment module
3 = No resit is available for the continuous assessment component
This module is category
Indicative Reading List

  • Jeffrey Wooldridge: 0, Introductory Econometrics: A modern approach, 8131524655
  • Stock, James H; Watson, Mark W: 0, Introduction to Econometrics, 935286350X
Other Resources

None
Programme or List of Programmes
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