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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:
Arts and Social Sciences
| Study level | Undergraduate |
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| Academic unit | Economics |
| Credit points | 6 |
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Prerequisites:
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ECMT2160 or ECMT2110 |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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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
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Modes of attendance (MoA)
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