Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Econometrics and Forecasting
Module Code	EF308
School	DCUBS
Online Module Resources
Module Teacher	Hiroyuki Kawakatsu
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

The purpose of this module is to introduce students to basic regression methods that are useful 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. Explain how to produce and evaluate forecasts using time series models
2. Carry out basic statistical inference procedures using regression models
3. Use statistical software to produce and evaluate forecasts of real economic time series data
4. Present results of statistical analyses

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

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

Linear regression.
ordinary least squares, Gauss-Markov theorem, hypothesis testing, robust inference, prediction.

Time series models.
autocorrelation, autoregressive models, dynamic forecasting, forecast evaluation.

Nonstationarity.
stationarity, deterministic and stochastic trends, structural breaks.

Dynamic causal effects.
distributed lag models, Granger causality, dynamic multipliers.

Assessment Breakdown
Continuous Assessment	50%	Examination Weight	50%

Course Work Breakdown
Type	Description	% of total	Assessment Date
In Class Test	Review of simple econometric techniques and properties of statistical estimators	5%	Week 4
Project	Estimate a simple linear regression model	10%	Week 5
In Class Test	Assess and make judgements about empirical statistical work	10%	Week 8
Assignment	Empirical project	25%	Week 11

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
This module is category 1

Indicative Reading List