Course Catalogue

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Econometrics develops knowledge and skills in Advanced Quantitative Methods and provides a solid foundation for further study in applied economics. The course content covers probability and statistics, as well as advanced concepts in regression and econometric estimation (ML, Bayesian inference).

10 x 2-hour lectures.

10 x 2-hour tutorials.

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ILO being assessed

This course aims to establish in depth the connection between developing a theoretical economic model and estimating an empirical econometric model, and it will provide a wide range of significant tools, informed by forefront development, necessary for testing economic hypotheses. The focus will be on the deep understanding of the theoretical properties of econometric estimators, such as consistency and asymptotic behaviour, as well as more practical issues such as identifiability of model parameters, computation, and general approaches to statistical inference using economic data.

By the end of this course students will be able to:

1. Critically analyse a wide range of the theoretical and practical issues associated with econometric models

2. Identify, conceptualize, define and motivate a series of estimators and estimation methodologies/algorithms, and their optimal use in various empirical scenarios

3. Demonstrate extensive, detailed and critical knowledge and understanding of concepts and ideas discussed in applied econometrics articles at the forefront.

4. Solve significant specialized applied projects, creatively using a wide range of computer-based packages

5. Work collaboratively in a group to produce a combined piece of coursework, by liaising with other class members, allocating tasks and co-ordinating group meetings.

6. Programme advanced algorithms in MATLAB that allow estimation and statistical inference.

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.