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.
| Original language | English |
|---|---|
| Pages (from-to) | 404-422 |
| Number of pages | 19 |
| Journal | Economic Systems Research |
| Volume | 31 |
| Issue number | 3 |
| Early online date | 9 Nov 2018 |
| DOIs | |
| Publication status | Published - 2019 |
Bibliographical note
The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission or the University of Aberdeen. The authors would like to thank Maria Espinosa, Sergio Gomez y Paloma, three reviewers and the editor for valuable comments, and Javier Alba (and the IPTS-IT department) for technical assistance.Keywords
- 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
- GAUSSIAN QUADRATURES
- ECONOMY