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 http://www.runmycode.org/companion/view/1116.

Keywords: Approximate Bayesian computation; Likelihood-free methods; Selection of statistics; Method of simulated moments (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (1)

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Working Paper: On Selection of Statistics for Approximate Bayesian Computing or the Method of Simulated Moments (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:100:y:2016:i:c:p:99-114

DOI: 10.1016/j.csda.2015.05.005

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