Computational Economics (ECON90055)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 1
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This subject introduces state-of-the-art computational techniques that benefit research in microeconomics, macroeconomics, econometrics, data administration and analysis. Students will learn to solve and estimate structural economic models and to apply these methods to substantive issues in various areas such as econometrics, industrial organisation, labour economics, economic history, and macroeconomics. Students will also learn how to handle spatial datasets for causal inference and undertake empirical analyses through efficient programming and probabilistic modelling. The course emphasises both theoretical knowledge of computational methods and practical skills.

Intended learning outcomes

By the end of this course, students will have received a detailed introduction to:

  • Apply fast probabilistic modelling and inference to flexible parametric models.
  • Create spatial data visualisation.
  • Use field-specific software platform for economic analysis.
  • Master linear algebra techniques for fast computation.
  • Solve nonlinear equations by using numerical techniques.
  • Solve and estimate structural models.
  • Carry out numerical dynamic programming.
  • Synthesize computational techniques with research.

Generic skills

  • High level of development: problem solving; collaborative learning; teamwork; application of theory to practice; use of computer software; numerical computing; interpretation and analysis; critical thinking;
  • Moderate level of development: written communication; evaluation of data and other information; statistical reasoning; receptiveness to alternative ideas;
  • Some level of development: oral communication; synthesis of data and other information; accessing data and other information from a range of sources.

Last updated: 4 March 2025