- Department: Economics and Related Studies
- Credit value: 20 credits
- Credit level: M
- Academic year of delivery: 2024-25
- See module specification for other years: 2023-24
This module introduces basic statistical and probability concepts and various econometric methods commonly used in quantitative analysis.
Occurrence | Teaching period |
---|---|
A | Semester 1 2024-25 |
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
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
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
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 100 |
None
Task | % of module mark |
---|---|
Closed/in-person Exam (Centrally scheduled) | 100 |
Feedback will be provided in line with University policy
Wooldridge, J., Introductory Econometrics: A Modern Approach, South Western.