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Unit information: Further Econometrics in 2018/19

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Unit name Further Econometrics
Unit code EFIMM0040
Credit points 15
Level of study M/7
Teaching block(s) Teaching Block 2 (weeks 13 - 24)
Unit director Dr. Stouli
Open unit status Not open

ECONM1022 Econometrics



School/department School of Economics, Finance and Management
Faculty Faculty of Social Sciences and Law


This unit aims to introduce students to some modern micro-econometric and time series techniques. Topics will include heteroscedasticity, asymptotic distribution theory, quantile regression and models with limited dependent variables, as well as an introduction to time series econometrics. The unit aims to build in students the ability to know, understand, and evaluate these tools and to apply them when undertaking novel research.

Intended learning outcomes

This unit provides a thorough and in-depth treatment of the basic concepts in microeconometrics and time series econometrics. The aim will be to equip students with tools necessary to be able to access the journal articles in the discipline, to evaluate them critically and to start independent research projects involving some recent methods. Students will also learn to the application of statistical software to these tools, its scope and limitations.

Teaching details

There will be lectures, exercise classes and computer classes. Coursework will consist of weekly exercises for formative assessment. Lectures will introduce and explain the different concepts and methods as well as their application and limitation whereas exercise classes will provide the opportunity to practice the selection and use of these methods as well as the application, scope and limitation of statistical software.

  • 16 hours lectures
  • 8 hours exercise lectures
  • 4 hours computes classes
  • 2.5 hours final exam
  • 119.5 hours individual study

Assessment Details

Summative assessment:

2.5-hour written exam. The exam will test the knowledge, selection, application and evaluation of tools and methods.

Formative assessment:

Worked exercises, class participation and discussion in exercise lectures and computer classes. These will provide further opportunities for feedback on the students’ progress.

Reading and References

  • J. Angrist & J.S. Pischke, Mostly Harmless Econometrics, Princeton.
  • A. Cameron & P. K. Trivedi, Microeconometrics. Methods and Applications, Cambridge.
  • R. Koenker, Quantile Regression, Cambridge.
  • M. Verbeek, A Guide to Modern Econometrics, (Fourth Ed.), J. Wiley and Sons.