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Econometrics for Research - ECO00111M

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  • Department: Economics and Related Studies
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24
    • See module specification for other years: 2024-25

Module summary

The module provides an advanced treatment of three selected topics in Econometrics that span the different areas of research of our PhD students

Module will run

Occurrence Teaching period
A Semester 1 2023-24

Module aims

  • A set of specialist lectures specialising in Econometric Theory
  • A complementary set of practical classes to teach the implementation of applied econometrics

Module learning outcomes

The topics taught in Applied Econometrics for Research can change from year to year, depending on what is considered to be most relevant and up to date. The current topics are:

  1. Methods for panel data analysis. Recent topics related to panel data analysis will be covered. At the end of this part, students should be able to analyse panel data by selecting appropriate models and relevant estimation methods.

  2. Methods for causal inference. We will cover methods for causal inference including topics such as Instrumental variable, Difference in Difference and Regression Discontinuity Design.

  3. Factor Models. We will cover principal components, dynamic factor models, identification, maximum likelihood estimation and the kalman filter. The module overviews the main applications of factor models: forecasting, missing observations, structural identification and counterfactual analysis. Real data applications in macroeconomics and finance will be presented

Indicative assessment

Task % of module mark
Essay/coursework 100

Special assessment rules

None

Additional assessment information

Project on one of the three topics that will involve:

  • Using the econometric software of choice to estimate an econometric model

  • Interpretating and communicating the implications of econometric output relating to estimation and inference.

  • Discussing the methods and results in relation to the relevant literature

Indicative reassessment

Task % of module mark
Essay/coursework 100

Module feedback

Feedback will be provided in line with University guidelines.

Indicative reading

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.

Angrist, J. D., & Pischke, J. S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton university press.

Hamilton, J. D. (2020). Time series analysis. Princeton university press.


The information on this page is indicative of the module that is currently on offer. The University constantly explores ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary. In some instances it may be appropriate for the University to notify and consult with affected students about module changes in accordance with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.