EconPapers    
Economics at your fingertips  
 

Gaussian Quadratures vs. Monte Carlo Experiments for Systematic Sensitivity Analysis of Computable General Equilibrium Model Results

Nelson Villoria () and Paul Preckel ()

Economics Bulletin, 2017, vol. 37, issue 1, 480-487

Abstract: Third-order Gaussian quadratures (GQ) approximate the mean and variance of model results allowing for computationally inexpensive sensitivity analysis to uncertainty in exogenous parameters. Unfortunately, commonly used GQ approaches restrict the marginal distributions of both parameters and results sacrificing valuable distributional information. Using higher order quadratures, or incorporating more uncertain exogenous parameters, rapidly increases the sample size, undermining the rationale for using GQ. In contrast, Monte Carlo methods directly approximate the distribution of model outcomes without restrictive distributional assumptions on exogenous parameters. We argue that current computing capabilities allow for wider use of Monte Carlo methods for conducting stochastic simulations.

Keywords: Sampling methods; Gaussian Quadratures; Monte Carlo; Stochastic modeling; Commodity markets (search for similar items in EconPapers)
JEL-codes: C6 C4 (search for similar items in EconPapers)
Date: 2017-03-20
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed

Downloads: (external link)
http://www.accessecon.com/Pubs/EB/2017/Volume37/EB-17-V37-I1-P43.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ebl:ecbull:eb-17-00008

Access Statistics for this article

More articles in Economics Bulletin from AccessEcon
Bibliographic data for series maintained by John P. Conley ().

 
Page updated 2020-10-02
Handle: RePEc:ebl:ecbull:eb-17-00008