A Monte Carlo filtering application for systematic sensitivity analysis of computable general equilibrium results. Issue 3 (3rd July 2019)

• Record Type: Journal Article
• Authors: Mary, Sébastien; Phimister, Euan; Roberts, Deborah; Santini, Fabien
• Is Part Of: Economic systems research. Volume 31:Issue 3(2019)
• Page Start: 404
• Page End: 422
• Publication Date: 2019-07-03
• Subjects: Monte Carlo filtering -- systematic sensitivity analysis -- computable general equilibrium model -- common agricultural policy -- rural development; Input-output analysis -- Periodicals; 339.23
• DOI: 10.1080/09535314.2018.1543182
• Languages: English
• ISSNs: 0953-5314

Abstract

ABSTRACT: 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.