- Student Records
Programme & Unit Catalogues

Department of Economics & International Development, Unit Catalogue 2007/08


EC50118 Econometrics: cross section & panel data

Credits: 6
Level: Masters
Semester: 1
Assessment: CW 50%, EX 50%
Requisites:
In taking this unit you cannot take EC50161
Aims: The aim of this unit is to provide students with a solid background in the analysis of "microdata" (individuals, households or firms). The unit covers both theoretical foundations and practical application with a focus on cross-sectional and panel data estimation methods. The emphasis throughout is on the specification, estimation, interpretation and testing of microeconometric models. A variety of practical applications will be considered, including labour supply decisions, occupational /political choice and employment decisions.
Learning Outcomes:
Students will have a critical understanding of a wide variety of econometric techniques especially as applied to cross section data and be able to undertake their own econometric analysis making use of those techniques with 'live' data.
Skills:
Ability to develop rigorous arguments through precise use of concepts and mathematical models (Taught/Facilitated/Assessed)
Ability to select, analyse and present numerical data using econometric packages (T/F/A)
Ability to select, summarise and synthesis written information from multiple sources (T/F/A)
Ability to select and use appropriate ideas to produce a coherent response to a pre-set question (T/F/A)
Comprehensive and scholarly written communication (T/F/A)
Concise and effective written communication (e.g. briefings / written exams) (T/F/A)
Effective oral communication (e.g. lecture question and answer) (F)
Ability to formulate a research question, then develop and present an original & coherent answer (T/F/A)
Ability to produce work to agreed specifications and deadlines (T/F/A).
Content:
The first part of the course will consist of a discussion of linear and non-linear estimation methods with a specific focus on OLS, instrumental variable and Maximum Likelihood estimation techniques. The second part of the course consists of looking at various applications of these techniques in practice. Some of the topics we will consider include discrete choice modelling techniques (binary and multiple-choice models) and limited dependent variable analysis (censored regression analysis, hazard modelling and count models). There will also be an introduction to panel data estimation techniques and a discussion of estimating treatment effects.