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ETC5440 · Statistical theory and practice

Official Handbook

2026 Handbook6 credit pointsLevel 5Department of Econometrics and Business Statistics

Last checked: 23 Aug 2026 UTC

Overview

The objective of this unit is to outline the general principles that underlie advanced methods of frequentist statistical inference. Building on maximum likelihood estimation (MLE), the unit provides a formal treatment of quasi-MLE, generalized method of moments (GMM), instrumental variable (IV) estimation, non-parametric methods and the bootstrap. The discussion is motivated by reference to econometric and statistical models and data, with insights provided into the theoretical properties and practical relevance of these alternative inferential techniques. The importance of asymptotic theory to the implementation of frequentist inference is highlighted, in a formal but intuitive way. Simulation experiments are used to reinforce the theoretical derivations.

Offerings

CampusTeaching periodMode
ClaytonFirst semesterTeaching activities are on-campus (ON-CAMPUS)

Assessment

The Handbook lists an examination for this unit.

#AssessmentTypeWeightHurdle
11 - ExerciseExercise40%
22 - ExaminationExamination60%

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

Requisites

prerequisite

  • ETC3580 — Advanced statistical modelling
  • ETC5340 — Principles of econometrics
  • ETC5341 — Applied econometrics
  • ETF5320 — Applied econometrics

Joined by OR.

prohibitions

  • ETC4400 — Statistical theory and practice

Learning outcomes

  1. build upon existing concepts developed in previous units and outline the principles underlying more advanced methods of inference
  2. highlight when and why different inferential methods are needed
  3. discuss the problem of endogeneity, and the link between generalized method of moments and instrumental variables
  4. discuss asymptotic theory in the context of all methods and explain its use in inference
  5. demonstrate the use of computer simulation to explore theoretical concepts.

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.

ActivityDuration
Workshops36 hours

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