Module: Introductory Econometrics

Module Specifications.

Current Academic Year 2024 - 2025

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Date posted: September 2024

Module Title	Introductory Econometrics
Module Code	EF5154 (ITS) / FBA1018 (Banner)
Faculty	DCU Business School	School	DCU Business School
Module Co-ordinator	Anh Vu
Module Teachers	Marjan Zhaf
NFQ level	9	Credit Rating	7.5
Pre-requisite	Not Available
Co-requisite	Not Available
Compatibles	Not Available
Incompatibles	Not Available

None
Resubmit take home assignments

Description

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

*Type*	*Hours*	*Description*
Workload	Full-time hours per semester
Lecture	36	lectures and programming demo
Independent Study	51.5	readings and review of lectures
Assignment Completion	60	take home assignments
Directed learning	40	preparation 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 Assessment	100%	Examination Weight	0%

Type	Description	% of total	Assessment Date
Course Work Breakdown
Assignment	take home assignments	50%	As required
In Class Test	in-class test	50%	As required

Reassessment Requirement Type
Resit arrangements are explained by the following categories: Resit category 1: A resit is available for both* components of the module. Resit category 2: No resit is available for a 100% continuous assessment module. Resit category 3: No resit is available for the continuous assessment component where there is a continuous assessment and examination element.
* ‘Both’ is used in the context of the module having a Continuous Assessment/Examination split; where the module is 100% continuous assessment, there will also be a resit of the assessment
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

None

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.