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Posterior Distribution of Nondifferentiable Functions

Author

Listed:
  • Toru Kitagawa

    (University College London)

  • José Luis Montiel Olea

    (Columbia University)

  • Jonathan Payne

    (New York University)

  • Amilcar Velez

    (Central Bank of Peru)

Abstract

This paper examines the asymptotic behavior of the posterior distribution of a possibly nondifferentiable function g(θ), where θ is a finite-dimensional parameter of either a parametric or semiparametric model. The main assumption is that the distribution of a suitable estimator θ̂n, its bootstrap approximation, and the Bayesian posterior for θ all agree asymptotically.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Toru Kitagawa & José Luis Montiel Olea & Jonathan Payne & Amilcar Velez, 2019. "Posterior Distribution of Nondifferentiable Functions," Working Papers 147, Peruvian Economic Association.
    • Handle: RePEc:apc:wpaper:147
    as

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    References listed on IDEAS

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    Cited by:

    1. Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," PIER Working Paper Archive 20-038, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
      • Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020. "Robust Forecasting," Papers 2011.03153, arXiv.org, revised Dec 2020.
    2. Gyungbae Park, 2024. "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers 2403.15934, arXiv.org.
    3. Kline, Brendan, 2024. "Classical p-values and the Bayesian posterior probability that the hypothesis is approximately true," Journal of Econometrics, Elsevier, vol. 240(1).
    4. Christopher D. Walker, 2024. "Semiparametric Bayesian Inference for a Conditional Moment Equality Model," Papers 2410.16017, arXiv.org.

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