ETC5340 · Principles of econometrics

Overview

This unit provides a formal treatment of the core principles underlying econometric and statistical analysis, with particular focus given to likelihood-based inference. Topics covered include the likelihood principle and maximum likelihood estimation; minimum variance unbiased estimation; maximum likelihood asymptotic distribution theory; likelihood-based hypothesis testing; The theoretical developments are supplemented by numerical results produced using computer simulation. Consideration is also given to the numerical optimisation techniques used to implement likelihood-based procedures in practice.

Offerings

Assessment

The Handbook lists an examination for this unit.

Assessment details may change. Please refer to the assessment information in Moodle closer to the start of the teaching period.

Requisites

Learning outcomes

1. consolidate the core principles underlying econometric and statistical analysis
2. understand and implement the technique of maximum likelihood estimation and develop an appreciation of the associated asymptotic distribution theory
3. understand and implement likelihood-based hypothesis testing
4. develop the skills needed to demonstrate and explore theoretical sampling properties using computer simulation
5. critically analyse the application of principles underlying econometric analysis.

Workload

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. You are expected to complete all pre-class activities prior to your scheduled class, and post-class activities should be completed after your scheduled class. Learning activities may include a combination of teacher directed, peer directed and online engagement activities.

Student experience, not official rules. Nothing here changes what the Handbook says.