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Module Specifications

Archived Version 2010 - 2011

Module Title Econometrics and Forecasting
Module Code EF308
School DCUBS

Online Module Resources

Module Co-ordinatorDr Hiroyuki KawakatsuOffice NumberQ232
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 (such as hypothesis testing and construction of confidence/prediction intervals) using regression models
3. use statistical software to produce and evaluate forecasts of real economic time series data
4. present results of statistical analyses using appropriate statistical displays (e.g. tables and graphs)



Workload Full-time hours per semester
Type Hours Description
Lecture24lecture and discussion
Laboratory20computer work
Assignment Completion31take home assignments
Independent Study50readings and review of lectures
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 Assessment100% Examination Weight0%
Course Work Breakdown
TypeDescription% of totalAssessment Date
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, 2nd,
  • Jeffrey Wooldridge: 0, Introductory Econometrics, 4th,
Other Resources

None
Programme or List of Programmes
AFBA in Accounting & Finance
BSBachelor of Business Studies
BSIBusiness Studies ( with INTRA )
BSSAStudy Abroad (DCU Business School)
BSSAOStudy Abroad (DCU Business School)
EBFBA in European Business (French)
EBGBA in European Business (German)
EBSBA in European Business (Spanish)
EBTBA in European Business (Trans.Studies)
ECSAStudy Abroad (Engineering & Computing)
ECSAOStudy Abroad (Engineering & Computing)
HMSAStudy Abroad (Humanities & Soc Science)
HMSAOStudy Abroad (Humanities & Soc Science)
IBLCBA in Inter. Business & Lang. (Chinese)
IBLFGBA in Inter. Business & Lang. (Fr/Ge)
IBLFSBA in Inter. Business & Lang. (Fr/Sp)
IBLGSBA in Inter. Business & Lang. (Ge/Sp)
IBLJBA in Inter. Business & Lang. (Japanese)
SHSAStudy Abroad (Science & Health)
SHSAOStudy Abroad (Science & Health)
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