Applied Economics

The economic drivers of production and trade are changing


Operating from real-world data, AIBE uses a variety of empirical techniques supported by economic theory to inform the development of business strategies and public policies on economic and social issues. Rather than utilise a single econometric model, researchers employ complimentary models in order to triangulate results and provide more robust recommendations. 


Our Applied Economics researchers have expertise in:

  • Economic modelling and forecasting
  • Business economics
  • Economics of innovation
  • Consumer and firm behaviour
  • Microeconomics
  • Macroeconomics
  • Labour and social economics
  • Environmental economics
  • Economics and law
  • Transport, freight and logistics.

Models and techniques

Specific economic models and analysis techniques include:

  • General equilibrium models:
    • Multi-sectoral general equilibrium models to capture sectoral linkages
    • Interaction and macroeconomic dynamic models to account for a closed macroeconomic system
    • Recursive dynamic general equilibrium models
    • Applications of the above to trade, environmental, and fiscal policy development
  • Regional and non-linear input-output models
  • Incursion assessment style models using Markov chains to analyse discrete events and actions
  • Structured decision-making systems for cost-benefit analyses
  • Optimised stochastic portfolio models for investments and technology diffusion
  • Malliavin Calculus - useful for taking derivatives in situations with uncertainty - in particular, option pricing
  • Production possibility frontier analysis
  • Game theoretic analysis including auction theory, Bertrand, Cournot, Stackelberg, asymmetric information, principal-agent problems

Financial modelling

AIBE also utilises financial models including:

  • Valuation methods (real options, Markov chains, debt sculpting)
  • Optimisation in decision theory (data envelopment analysis, multi-criteria assessments, simulated annealing, genetic algorithms)
  • Machine learning and applications (PCA, decision trees, minimum spanning trees, deep learning and neural networks)
  • Risk assessment (credit + market + operational risk techniques, Bayesian updating, copula methods, distribution theory).

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