Skip to main content
Unit of study_

ECMT3170: Computational Econometrics

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.

Code ECMT3170
Academic unit Economics
Credit points 6
ECMT2160 or ECMT2110

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 outlines

Unit outlines will be available 1 week before the first day of teaching for the relevant session.

There are no unit outlines available online for the current year.