• Offered by Research School of Economics
  • ANU College ANU College of Business and Economics
  • Course subject Econometrics
  • Academic career UGRD
  • Course convener
    • Dr Thomas Tao Yang
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in Second Semester 2020
    See Future Offerings

All activities that form part of this course will be delivered remotely

The course studies important extensions of the linear regression model. Topics include: endogeneity, binary dependent variables, time series regressions and panel data estimation. This is a hand-on course with a focus on applications in economics as well as business. A standard statistical software will be used during computer sessions, no special programming skills are required.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. demonstrate an understanding of the challenges of empirical modelling in economics and business
  2. demonstrate an understanding of the shortcomings of the standard linear regression model
  3. apply important extensions to the linear regression model
  4. express new econometric methods mathematically
  5. demonstrate clarity of thought regarding the relationship between data, model and estimation in econometrics
  6. use statistical software to study actual data sets

Indicative Assessment

  1. Assignments (30) [LO 1,2,3,4,5,6]
  2. Final examination (70) [LO 1,2,3,4,5,6]

In response to COVID-19: Please note that Semester 2 Class Summary information (available under the classes tab) is as up to date as possible. Changes to Class Summaries not captured by this publication will be available to enrolled students via Wattle. 

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

Students taking this course are expected to commit at least 10 hours per week consisting of 2 hours of lectures and a 1 hour of computer tutorial as well as 7 hours of private study

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must have completed ECON1101 Microeconomics 1; and have completed STAT2008 Regression Modelling or STAT2014 Regression Modelling for Actuaries or EMET2007 Econometrics I: Econometrics Methods. Incompatible with EMET6008.

Prescribed Texts

see Class Summary and Wattle site.

Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
3
Unit value:
6 units

If you are an undergraduate student and 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). You can find your student contribution amount for each course at Fees.  Where there is a unit range displayed for this course, not all unit options below may be available.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2020 $4320
International fee paying students
Year Fee
2020 $5760
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

Second Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
7322 27 Jul 2020 03 Aug 2020 31 Aug 2020 30 Oct 2020 In Person View

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