Module: Econometrics & Forecasting

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Econometrics & Forecasting
Module Code	ECO1012 (ITS: EF308)
Faculty	DCU Business School	School	DCU Business School
NFQ level	8	Credit Rating	5

Description

The purpose of this module is to introduce students to basic regression methods that are used in analyzing social science data. The emphasis is on the application of statistical methods to analyze economic and/or financial data. In this module students will develop knowledge and skills in applying statistical methods to analyze data, using appropriate statistical software to analyze data, using regression models to test hypotheses, making and evaluating predictions based on statistical models. Students will participate in the following learning activities: 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. carry out basic statistical inference procedures using regression models
2. use statistical software to produce and evaluate forecasts of real economic time series data
3. select appropriate econometric techniques to analyse particular data sets
4. evaluate empirical statistical work and critically assess econometric approaches
5. explain how to produce and evaluate forecasts using time series models

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	20	Lecture and discussion
Directed learning	20	Labs and computer work
Assignment Completion	35	Take home assignments
Independent Study	50	Readings and review of lectures; online learning exercises
Total Workload: 125

Section Breakdown
CRN	20249	Part of Term	Semester 2
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Michael Dowling	Module Teacher

Type	Description	% of total	Assessment Date
Assessment Breakdown
Assignment	Complete two worksheets	100%	n/a

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

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

Introduction to econometrics
review of statistical inference, data and data types, simple linear regression, hypothesis testing, dummy variables, the classical Linear Model.

Using econometric models
tests of model specification, multicollinearity, heteroscedasticity, serial correlation, stochastic regressors

Topics in time series econometrics
stationarity, cointegraton and error correction models, Granger causality, ARCH & GARCH, forecasting using regression and ARIMA models

Indicative Reading List

Books:

Stock, James H; Watson, Mark W: 0, Introduction to econometrics,
Jeffrey M Wooldridge: 0, Introductory Econometrics,

Articles:
None

Other Resources

None