Registry

Module Specifications

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

Module Title	Econometrics
Module Code	EF323
School	DCUBS
Online Module Resources
NFQ level	8	Credit Rating	5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

This module introduces students 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. Students will develop knowledge and skills in applying relevant statistical methods to analyze data, using appropriate statistical software to analyze data, interpreting results from conducting statistical inference such as testing hypotheses and/or making predictions, critically evaluating 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. Carry out basic statistical inference procedures (such as hypothesis testing and construction of confidence/prediction intervals) using regression models
2. Explain the uses of instrumental variables and panel data methods for conducting causal inference using observational data
3. Perform basic data analysis using appropriate statistical software
4. Present results of statistical analyses using appropriate statistical displays (e.g. tables and graphs)

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	24	lecture and discussion
Independent learning	51	readings and review of lectures
Lab	10	computer work
Assignment	20	take home assignments
Examination	20	preparation for class test
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, prediction, robust inference..

Instrumental variables.
instrumental relevance, instrument exogeneity, choice of instruments, Hausman test, over-identification test..

Panel data models.
(one-way and two-way) fixed effects model, difference-in-differences..

Empirical applications.
review and evaluation of empirical literature that applies the statistical methods discussed in class.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

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

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