Economics at your fingertips  

On selection of statistics for approximate Bayesian computing (or the method of simulated moments)

Michael Creel () and Dennis Kristensen ()

Computational Statistics & Data Analysis, 2016, vol. 100, issue C, 99-114

Abstract: A cross validation method for selection of statistics for Approximate Bayesian Computing, and for related estimation methods such as the Method of Simulated Moments, is presented. The method uses simulated annealing to minimize the cross validation criterion over a combinatorial search space that may contain an extremely large number of elements. A first simple example, for which optimal statistics are known from theory, shows that the method is able to select these optimal statistics out of a large set of candidate statistics. A second example of selection of statistics for a stochastic volatility model illustrates the method in a more complex case. Code to replicate the results, or to use the method for other applications, is provided at

Keywords: Approximate Bayesian computation; Likelihood-free methods; Selection of statistics; Method of simulated moments (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only.

Related works:
Working Paper: On Selection of Statistics for Approximate Bayesian Computing or the Method of Simulated Moments (2015) Downloads
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:

DOI: 10.1016/j.csda.2015.05.005

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().

Page updated 2023-01-03
Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:99-114