Registry
Module Specifications
Archived Version 2021 - 2022
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Description This module builds on the quantitative stream of the BSIF degree programme. In particular it extends the material covered in Econometrics 1, honing the skills required in multivariate regression analyses. You will learn how to identify and deal with problems that arise in practice when working with regression models, eg choice of functional form, non-adherence to Gauss Markov assumptions and interpretation of results. You will also start to work with more advanced discrete choice models that require MLE estimation. The course will involve practical data analysis using STATA and students will submit an independent group project involving analysis of data and interpretation of results | |||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Identify and correct for common problems in practical regression analysis and issues arising from non-adherence to Gauss Markov assumptions 2. Select appropriate econometric techniques to analyse continuous and discrete dependent variables 3. Conduct econometric analysis using STATA and comment on results 4. Evaluate empirical statistical work and assess econometric approaches | |||||||||||||||||||||||||||||||||||||
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 |
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Indicative Content and
Learning Activities Revision of Multivariate Regression theoryReview of semester 1 including exam questionsIntroduction to STATABasic STATA commandsCommon Practical Issues in Multivariate RegressionSpecification Issues and Functional form, Multicollinearity, Use of Dummy VariablesNon-adherence to GM assumptionsHeteroscedasticity Autocorrelation Stochastic RegressorsLimited Dependent VariablesLogit/probit Ordered probit Multinomial logit | |||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||
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