Accessibility statement

Statistics and Econometrics - ECO00094M

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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

This module introduces basic statistical and probability concepts and various econometric methods commonly used in quantitative analysis.

Module will run

Occurrence Teaching period
A Semester 1 2023-24

Module aims

The module covers the following topics:

  • Probability, random variables, point and interval estimation, small and large sample properties of estimators, hypotheses testing;

  • Simple linear regression models, the OLS estimator, t and F tests, properties of the OLS estimator, Gauss-Markov theorem

  • Multiple linear regression models, heteroskedasticity, autocorrelation, specification errors, dummy variables, variables of interactions

  • Endogeneity and instrumental variable estimators

  • Binary dependent variable models and maximum likelihood estimators

  • Treatment effects and difference-in-difference estimators

Module learning outcomes

Having successfully completed this module you will be able to:

  • demonstrate understanding of key statistical concepts

  • select the appropriate statistical models for the data set, estimate them and perform appropriate statistical tests using statistical computer package software.

  • analyse, interpret and summarise estimation and inference results and present them in an accessible manner to the audience

Module content

Week No. and Contents (4 hours each week, including seminars/practical’s)

1. Basic probabilities and random variables

2. Joint distributions, linear combinations, sampling distributions

3. Point and interval estimation, Hypothesis testing

4. Simple linear regression: OLS estimator and its properties

5. Multiple linear regression: Estimation

6. Multiple linear regression: Inference

7. Multiple linear regressions with binary, interactive variables, squared variables

8. Multiple linear regression: Heteroskedastic errors, specification tests, time-series data

9. Multiple linear regression: Endogeneity and 2SLS estimation

10. Introduction to Treatment effects: randomised trial, difference-in-difference estimator

11. Maximum Likelihood Estimation and Binary Choice Models

Indicative assessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Special assessment rules

None

Indicative reassessment

Task % of module mark
Closed/in-person Exam (Centrally scheduled) 100

Module feedback

Feedback will be provided in line with University policy

Indicative reading

Wooldridge, J., Introductory Econometrics: A Modern Approach, South Western.


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.