Registry

Module Specifications

Archived Version 2022 - 2023

Module Title
Module Code
School
Online Module Resources
NFQ level	9	Credit Rating	7.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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	%	Examination Weight	%

Type	Description	% of total	Assessment Date
Course Work Breakdown

Reassessment Requirement
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
Unavailable

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

Programme or List of Programmes

Archives: