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Unit of study_

ECMT3170: Computational Econometrics

2020 unit information

This unit provides an introduction to modern computationally intensive algorithms, their implementation and application for carrying out statistical inference on econometric models. Students will learn modern programming techniques such as Monte Carlo simulation and parallel computing to solve econometric problems. The computational methods of inference include Bayesian approach, bootstrapping and other iterative algorithms for estimation of parameters in complex econometric models. Meanwhile, students will be able to acquire at least one statistical programming language.

Unit details and rules

Managing faculty or University school:

Economics

Code ECMT3170
Academic unit Economics
Credit points 6
Prerequisites:
? 
ECMT2160 or ECMT2110
Corequisites:
? 
None
Prohibitions:
? 
None
Assumed knowledge:
? 
None

At the completion of this unit, you should be able to:

  • LO1. demonstrate proficiency in the use of programming software
  • LO2. demonstrate increased range of econometric techniques for use in research and applied work
  • LO3. critically evaluate underlying assumption and theories in econometrics
  • LO4. coherently communicate to a professional standard.

Unit availability

This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.

The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.

There are no availabilities for this year.
Session MoA ?  Location Outline ? 
Semester 1 2020
Normal day Camperdown/Darlington, Sydney

Modes of attendance (MoA)

This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.