- Code EMET4314
- Unit Value 6 units
The course covers advanced estimation methods in econometrics. Specific topics include: projections and ordinary least squares estimation; endogeneity; instrumental variables and two stage least squares estimation; maximum likelihood estimation of models with limited dependent variables. The course is primarily theoretical and looks at various estimators and their finite sample and asymptotic properties.
Upon successful completion, students will have the knowledge and skills to:
- define OLS, IV and maximum likelihood estimators mathematically;
- derive and examine finite sample and asymptotic properties of these estimators analytically;
- demonstrate an understanding of the strengths and limitations of the different estimators;
- employ linear algebra in key econometric derivations;
- apply econometric theory to concrete examples in economics;
- The assessment consists of two parts: assignments and a final exam. (100) [LO 1,2,3,4,5]
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10 hours per week including class time
Requisite and Incompatibility
You will need to contact the Research School of Economics to request a permission code to enrol in this course.
See Class Summary and Wattle site.
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
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Offerings, Dates and Class Summary Links
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.