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

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  • Creel, Michael
  • Kristensen, Dennis

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.

Suggested Citation

  • Creel, Michael & Kristensen, Dennis, 2016. "On selection of statistics for approximate Bayesian computing (or the method of simulated moments)," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 99-114.
    • Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:99-114
    DOI: 10.1016/j.csda.2015.05.005
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    Citations

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    Cited by:

    1. Valerio Scalone, 2018. "Estimating Non-Linear DSGEs with the Approximate Bayesian Computation: an application to the Zero Lower Bound," Working papers 688, Banque de France.
    2. Michael Creel & Jiti Gao & Han Hong & Dennis Kristensen, 2016. "Bayesian Indirect Inference and the ABC of GMM," Monash Econometrics and Business Statistics Working Papers 1/16, Monash University, Department of Econometrics and Business Statistics.
    3. Vincent Boucher, 2017. "The Estimation of Network Formation Games with Positive Spillovers," Cahiers de recherche 1710, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.
    4. Creel, Michael, 2017. "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, vol. 2(C), pages 36-49.
    5. Michael Creel, 2016. "Neural Nets for Indirect Inference," UFAE and IAE Working Papers 960.16, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC), revised 18 Jul 2016.
    6. Boucher, Vincent, 2020. "Equilibrium homophily in networks," European Economic Review, Elsevier, vol. 123(C).

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    More about this item

    Keywords

    Approximate Bayesian computation; Likelihood-free methods; Selection of statistics; Method of simulated moments;
    All these keywords.

    JEL classification:

    • E24 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models

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