A Monte Carlo filtering application for systematic sensitivity analysis of computable general equilibrium results

Sebastien Mary (Corresponding Author), Euan Phimister, Deborah Roberts, Fabien Santini

Research output: Contribution to journalArticle

1 Citation (Scopus)


Parameter uncertainty has fuelled criticisms on the robustness of results from computable general equilibrium models. This has led to the development of alternative sensitivity analysis approaches. Researchers have used Monte Carlo analysis for systematic sensitivity analysis because of its flexibility. But Monte Carlo analysis may yield biased simulation results. Gaussian quadratures have also been widely applied, although they can be difficult to apply in practice. This paper applies an alternative approach to systematic sensitivity analysis, Monte Carlo filtering and examines how its results compare to both Monte Carlo and Gaussian quadrature approaches. It does so via an application to rural development policies in Aberdeenshire, Scotland. We find that Monte Carlo filtering outperforms the conventional Monte Carlo approach and is a viable alternative when a Gaussian quadrature approach cannot be applied or is too complex to implement.
Original languageEnglish
Pages (from-to)404-422
Number of pages19
JournalEconomic Systems Research
Issue number3
Early online date9 Nov 2018
Publication statusPublished - 2019



  • rural development
  • Monte Carlo Filtering
  • Systematic Sensitivity Analysis
  • Computable General Equilibrium model
  • Common Agricultural Policy
  • Pillar 2
  • Monte Carlo filtering
  • computable general equilibrium model
  • systematic sensitivity analysis
  • common agricultural policy

Cite this