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BAEcon Economics and Finance

Year of entry: 2021

Course unit details:
Micro Econometrics

Unit code ECON30342
Credit rating 10
Unit level Level 3
Teaching period(s) Semester 2
Offered by Economics
Available as a free choice unit? Yes


This course unit makes use of the basic econometric tools learned in Econ20110 Econometrics and applies them to modern microeconometric issues. Microeconometrics is the application of regression techniques to estimating causal effects using cross-section or panel data sampled from microeconomic agents. The course will be of interest to those studying microeconomics or related areas (e.g. labour economics, public economics, and development economics) and will be useful to students who wish to pursue more advanced applied economics co­urses later in their career.


Unit title Unit code Requirement type Description
Advanced Econometrics ECON31031 Co-Requisite Compulsory
co-requisites: ECON31031



The aim of this course to further develop students’ knowledge of modern microeconometric techniques, and so develop the tools required to analyse datasets covering relating to microeconomic units  (individuals, households or firms) observed at a single point in time or over time multiple time periods. It focuses on causes of bias/inconsistency, and therefore non-causality, namely endogeneity. These causes are omitted variables, measurement error, simultaneity and self-selection.


Learning outcomes

After completing this unit, students will:

(i) be able to demonstrate a clear understanding of how the regression model is used when analysing cross-section data;

(ii) know how to use, and interpret the output from the econometric package Stata;

(iii) understand what is meant by a causal effect;

(iv) appreciate the various difficulties in estimating causal effects, and understand the way in which econometric methods, in combination with economic models, can address these difficulties when using cross-sectional and panel data.


  1. Introduction: types of data; panel v (pooled) cross sections.
  2. Causality and unbiased estimation:
    1. Wooldridge’s MRL1-4 & multiple regression;
    2. Simple regression with RHS dummy: y on {1, d}. The analogy principle (estimation in the population);
    3. Consistency.
  3. Endogeneity:
    1. Omitted variables bias (OVB), long and short regressions;
    2. Measurement error (ME);
    3. Simultaneity.
  4. Selection bias, treatment effects, and the Potential Outcomes framework.
  5. Inference (hypothesis testing)
    1. Traditional inference (Wooldridge’s MLR5 & MLR6);
    2. Heteroskedasticity and Robust inference;
    3. Clustering (Moulton formula only).
  6. Modelling using Multiple regression.  y on multi-category dummies (firm-size and earnings) and fitting a quadratic (graduate earnings differential and age).
  7. Lectures and tutorial classes.

Employability skills

Analytical skills
Synthesis and analysis of data and critical reflection and evaluation.
Computer literacy and the application of theoretical knowledge.

Assessment methods

80%                  Exam

20%                  Mid-term test


Feedback methods

  • Tutorial feedback.
  • Office hours.
  • Modl answers

Recommended reading

The main reading is:

• Wooldridge Introductory Econometrics, seventh edition (chapters 1-7, 13, 15).


Other texts referred to:

  • Angrist, J. & Pischke, J.-S.  (2009), Mostly Harmless Econometrics:  An Empiricist's Companion, Princeton University Press, Princeton, NJ. 
  • Angrist, J. & Pischke, J.-S.  (2015), Mastering 'Metrics: The Path From Cause To Effect, Princeton University Press, Princeton.
  • Stock, J. & Watson, M. (2012), Introduction to Econometrics, third edn, Pearson.  Acronym.
  • Verbeek, M. (2017), A Guide to Modern Econometrics, fifth edn, Wiley. 


PDF files of the slides will be available for download from the course BB site before each lecture. See Blackboard Site for arrangements now there is online teaching only.



Study hours

Independent study hours
Independent study 0

Teaching staff

Staff member Role
Martyn Andrews Unit coordinator

Additional notes

For every 10 course unit credits we expect students to work for around 100 hours. This time generally includes any contact times (online or face to face, recorded and live), but also independent study, work for coursework, and group work. This amount is only a guidance and individual study time will vary.

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