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Kitagawa, Toru
Montiel-Olea, Jose-Luis
Payne, Jonathan Aidan
Year of Publication: 
Series/Report no.: 
cemmap working paper, Centre for Microdata Methods and Practice CWP20/16
This paper examines the asymptotic behavior of the posterior distribution of a possibly nondifferentiable function g(theta), where is a finite dimensional parameter. The main assumption is that the distribution of the maximum likelihood estimator theta_n, its bootstrap approximation, and the Bayesian posterior for all agree asymptotically. It is shown that whenever g is Lipschitz, though not necessarily differentiable, the posterior distribution of g(theta) and the bootstrap distribution of g(theta_n) coincide asymptotically. One implication is that Bayesians can interpret bootstrap inference for g(theta) as approximately valid posterior inference in a large sample. Another implication - built on known results about bootstrap inconsistency - is that the posterior distribution of g(theta) does not coincide with the asymptotic distribution of g(theta_n) at points of nondifferentiability. Consequently, frequentists cannot presume that credible sets for a nondifferentiable parameter g(theta) can be interpreted as approximately valid confidence sets (even when this relation holds true for theta).
Bernstein von-Mises Theorem
Directional Differentiability
Posterior Inference
Document Type: 
Working Paper

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