Statistics and Econometrics - ECO00094M
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 |
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A | Semester 1 2025-26 |
Module aims
The module covers the following topics:
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Probability, random variables, point and interval estimation, small and large sample properties of estimators, hypotheses testing;
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Simple linear regression models, the OLS estimator, t and F tests, properties of the OLS estimator, Gauss-Markov theorem
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Multiple linear regression models, heteroskedasticity, autocorrelation, specification errors, dummy variables, variables of interactions
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Endogeneity and instrumental variable estimators
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Binary dependent variable models and maximum likelihood estimators
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Treatment effects and difference-in-difference estimators
Module learning outcomes
Having successfully completed this module you will be able to:
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demonstrate understanding of key statistical concepts
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select the appropriate statistical models for the data set, estimate them and perform appropriate statistical tests using statistical computer package software.
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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 |
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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.