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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 |
|
Prerequisites:
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ECMT2160 or ECMT2110 |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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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 outlines
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There are no unit outlines available online for the current year.
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