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Moment conditions and Bayesian nonparametrics

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  • Bornn, Luke
  • Neil Shephard
  • Reza Solgi

Abstract

Models phrased through moment conditions are central to much of modern inference. Here these moment conditions are embedded within a non‐parametric Bayesian set‐up. Handling such a model is not probabilistically straightforward as the posterior has support on a manifold. We solve the relevant issues, building new probability and computational tools by using Hausdorff measures to analyse them on real and simulated data. These new methods, which involve simulating on a manifold, can be applied widely, including providing Bayesian analysis of quasi‐likelihoods, linear and non‐linear regression, missing data and hierarchical models.
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  • Bornn, Luke & Neil Shephard & Reza Solgi, 2016. "Moment conditions and Bayesian nonparametrics," Working Paper 360971, Harvard University OpenScholar.
    • Handle: RePEc:qsh:wpaper:360971
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    File URL: http://scholar.harvard.edu/shephard/node/360971
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    Cited by:

    1. Gael M. Martin & David T. Frazier & Christian P. Robert, 2022. "Computing Bayes: From Then `Til Now," Monash Econometrics and Business Statistics Working Papers 14/22, Monash University, Department of Econometrics and Business Statistics.
    2. Luo, Yu & Graham, Daniel J. & McCoy, Emma J., 2023. "Semiparametric Bayesian doubly robust causal estimation," LSE Research Online Documents on Economics 117944, London School of Economics and Political Science, LSE Library.
    3. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    4. Yusuke Narita & Kohei Yata, 2021. "Algorithm is Experiment: Machine Learning, Market Design, and Policy Eligibility Rules," Working Papers 2021-022, Human Capital and Economic Opportunity Working Group.
    5. Isaiah Andrews & Anna Mikusheva, 2022. "Optimal Decision Rules for Weak GMM," Econometrica, Econometric Society, vol. 90(2), pages 715-748, March.

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