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Applied Microeconometrics - ECO00092M

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  • Department: Economics and Related Studies
  • Module co-ordinator: Prof. Cheti Nicoletti
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2023-24

Module summary

The module will cover econometric methods that are essential for empirical analysis in microeconomics: e.g. linear and non-linear models, endogeneity issues, panel data, missing data and causal inference methods. Applied empirical examples will be provided.

Module will run

Occurrence Teaching cycle
A Semester 2 2023-24

Module aims

The module is designed to teach students econometric techniques that are essential for empirical analysis in microeconomics. The focus is on building basic research skills, including learning how to apply these techniques in practice, and on reading, interpreting and understanding empirical research. The module will cover key microeconometric estimation methods, such as panel data methods and models for limited dependent variables, as well as quasi-experimental econometric methods such as instrumental variables and differences in difference approaches. It will provide a broad range of empirical examples from labour economics, education, health economics and industrial organisation. Students will learn to use Stata to estimate different types of models, test assumptions, choose between different models and interpret results. The module will provide basic coding skills in Stata that are applicable to a broad set of coding platforms and statistical software. This will be invaluable for any empirical MSc dissertation topic and also for any job which involves the use of data for economic analysis.

Module learning outcomes

Given the extensive use of individual/household data sources in applied microeconomic analysis, it has become increasingly important to understand the techniques available to the microeconometrician in applied research. Moreover, it is just as important to be aware of the limitations and pitfalls associated with each microeconometric technique. The purpose of this module is to provide the applied economist with sufficient background of modern microeconometrics to choose techniques suited both to the data and to the economic model. Also, the lectures provide the opportunity to gain experience of empirical analysis using Stata software.

Module content

Outline syllabus (TBC)

  1. Interpretation of linear and nonlinear regressions

  2. Randomized Experiments

  3. Regression and causality

  4. Propensity score methods

  5. Instrumental Variable Estimation and Natural Experiment

  6. Extension of instrumental variable

  7. Differences-in-Differences

  8. Panel data analysis

  9. Missing data and sample selection

  10. How to start a research project

  11. How to write up the results of a project

Assessment

Task Length % of module mark
Essay/coursework
Applied Microeconometrics		 N/A 100

Special assessment rules

None

Reassessment

Task Length % of module mark
Essay/coursework
Applied Microeconometrics		 N/A 100

Module feedback

Feedback will be provided in line with University policy

Indicative reading

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

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


Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata (Vol. 2). College Station, TX: Stata press.


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring 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 by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.