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Econometric Theory - ECO00033I

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• Department: Economics and Related Studies
• Credit value: 20 credits
• Credit level: I
• Academic year of delivery: 2024-25
  • See module specification for other years: 2023-24

Module summary

The module gives student a thorough introduction to the theoretical concepts underlying modern Econometrics.

Related modules

Additional information

Prerequisite modules: Probability and Statistics, Mathematics for Economists

Co- requisite: Econometrics 

Module will run

Occurrence	Teaching period
A	Semester 2 2024-25

Module aims

The module covers the following topics:

• Distribution Theory: Relations between normal, chi-square, F, and t distributions. Multivariate normal distributions, marginal and conditional normal distributions.

• Asymptotic theory: Limits, continuous functions, law of large numbers, convergence in probability, convergence in distribution and the central limit theorem.

• The Classical Linear Regression Model: Matrix algebra, Ordinary Least Squares (OLS) estimator, F and t tests

• Maximum Likelihood (ML) Estimation: ML estimator, Cramer-Rao lower bound, Neyman-Pearson lemma, likelihood ratio, Wald, and LM tests.

Module learning outcomes

On successfully completing the module the student will be able to:

• apply key concepts and methods from statistical theory relevant for economic data analysis

choose statistical models and tests relevant for the analysis of small and large datasets

Module content

Subject to changes:

1. Finite sample results: review matrix, least squares

2. gauss Markov theorem

3. F test

4. Finite sample results: MLE, Information, MVUE

5. Elements of Asymptotic theory: review sequence, limits, boundedness, lim sup

6. Convergence in distribution, Convergence in probability, Slutsky, cmt, WLLN, iidCLT (and inid/dependent data?

7. asymptotic properties of OLS estimator: consistency and asymptotic normality

8. 2sls estimator and its asymptotic properties.

9. asymptotic properties of MLE estimator: consistency and asymptotic normality

10. MLE of a parameter vector and Wald, LR and LM tests, with the regression example.

11. LM test for error serial correlation or intro to times-series analysis

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

For the formative work students are set exercises prior to the seminars, and submit at least one piece of work to be marked which is returned within 25 working days of submission.

For the summative assessment a breakdown of marks by question will be provided also within 25 working days of the exam.

Indicative reading

Bartle, R.G., Sherbert, D.R., Introduction to Real Analysis, Wiley.

Robert V. Hogg, R.V., McKean, J.W., Craig, A.T., 2019, Introduction to Mathematical Statistics, 8th ed., Pearson.

White, H., Asymptotic Theory for Econometricians, revised edition, Academic Press.

Abadir and Magnus, Matrix Algebra, CUP.

Johnston and Dinardo, Econometric Methods, McGrow-Hill.