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Module Specifications..

Current Academic Year 2023 - 2024

Please note that this information is subject to change.

Module Title Introductory Econometrics
Module Code EF5154
School DCUBS
Module Co-ordinatorSemester 1: Hiroyuki Kawakatsu
Semester 2: Hiroyuki Kawakatsu
Autumn: Hiroyuki Kawakatsu
Module TeachersHiroyuki Kawakatsu
NFQ level 9 Credit Rating 7.5
Pre-requisite None
Co-requisite None
Compatibles None
Incompatibles None
Resubmit take home assignments

This module is an introduction to the theory and practice of econometric inference using social science data. The emphasis is on the application of statistical methods to analyze observational data typically used in finance. Students will develop knowledge and skills in applying relevant statistical methods to analyze data, use appropriate statistical software to analyze data, interpret results from conducting statistical inference such as testing hypotheses and/or making predictions, critically evaluate empirical results reported in journal articles. Students will attend and participate in lectures, familiarize themselves with the use of statistical software, work on take home assignments that replicate some previous empirical research, critically evaluate some of the literature in empirical research.

Learning Outcomes

1. understanding of basic probability and statistics concepts.
2. use regression models for statistical inference
3. use appropriate statistical software to conduct statistical analysis of real financial data
4. use appropriate statistical displays (tables and graphs) to communicate results of statistical analyses

Workload Full-time hours per semester
Type Hours Description
Lecture36lectures and programming demo
Independent Study51.5readings and review of lectures
Assignment Completion60take home assignments
Directed learning40preparation for class test
Total Workload: 187.5

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

Probability and statistics
Review of relevant probability and statistics concepts

Linear regression
OLS, Gauss-Markov theorem, hypothesis testing, prediction, robust inference.

Statistical computing
use of statistical software for analyzing real data

Statistical displays
presenting analysis results in appropriate table or graph form

Finance applications
linear asset pricing models, event study methodology

Assessment Breakdown
Continuous Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
Assignmenttake home assignments50%As required
In Class Testin-class test50%As required
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 1
Indicative Reading List

  • James Stock and Mark Watson: 0, Introduction to Econometrics, 3rd,
  • Jeffrey Wooldridge: 0, Introductory Econometrics, 6th,
  • David Freedman: 0, Statistical Models,
Other Resources

Module for new MSc in Finance as approved by Programme Board 26th March 2014. Submitting final document for T&L to Jonathan 28th March 2014.
Programme or List of Programmes
MFCMMSc in Finance

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