Registry
Module Specifications
Archived Version 2018 - 2019
| |||||||||||||||||||||||||||||||||||||
Description This module builds on the quantitative stream of the BSIF programme, in particular on Statistics and Data Analysis, to present a more in-depth approach to Econometrics and provide a basis for more advanced theory and techniques encountered by students in the final semester. You will learn how to deal with problems that arise in practice when working with regression models, eg choice of functional form and interpretation of results. The course emphasises the fundamental conceptual and theoretical foundations of Econometrics and gives an opportunity to apply the theory practically using software commonly employed by Economics and Finance specialists. | |||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Define and apply concepts in Probability Theory, Hypothesis Testing and Inference in a regression context. 2. Explain the Gauss Markov Theorem and the theoretical assumptions required to produce BLUE estimators in OLS 3. Explain the underpinnings of MLE and under what conditions MLE and OLS produce equivalent estimates. 4. Identify and solve basic practical problems arising in applied regression analysis 5. Plan an applied econometrics research project to be conducted using financial, economics or other social science based data | |||||||||||||||||||||||||||||||||||||
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 PreliminariesProbability theory Common Probability Distributions and Hypothesis testing Rules on Expectations, Variance, CovarianceReview of OLSi) Review of Single variable regression using OLS estimation ii) Multivariate regressionGauss-Markov Theorem and BLUE estimatesWhat is meant by BLUE? Prove Gauss Markov Theorem.Maximum Likelihood EstimationMaximum Likelihood Estimation - underpinnings and comparison with OLSPractical issuesIntroduction to general problems in practical work; Relaxation of Gauss Markov Assumptions; Solutions and corrections where possible. (i) Functional form (ii) Omitted variables (iii) Multicollinearity (iv) Heteroscedasticity | |||||||||||||||||||||||||||||||||||||
| |||||||||||||||||||||||||||||||||||||
Indicative Reading List
| |||||||||||||||||||||||||||||||||||||
Other Resources None | |||||||||||||||||||||||||||||||||||||
Programme or List of Programmes | |||||||||||||||||||||||||||||||||||||
Archives: |
|