Module: Introductory Econometrics

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-ordinator	Semester 1: Hiroyuki Kawakatsu Semester 2: Hiroyuki Kawakatsu Autumn: Hiroyuki Kawakatsu
Module Teachers	Hiroyuki Kawakatsu
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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; 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

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.

Programme or List of Programmes

MFCM	MSc in Finance

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