Skip to main content

RISK OF BAYESIAN INFERENCE IN MISSPECIFIED MODELS, AND THE SANDWICH COVARIANCE MATRIX

Author(s): Mueller, Ulrich K.

Download
To refer to this page use: http://arks.princeton.edu/ark:/88435/pr1rq8k
Abstract: It is well known that, in misspecified parametric models, the maximum likelihood estimator (MLE) is consistent for the pseudo‐true value and has an asymptotically normal sampling distribution with “sandwich” covariance matrix. Also, posteriors are asymptotically centered at the MLE, normal, and of asymptotic variance that is, in general, different than the sandwich matrix. It is shown that due to this discrepancy, Bayesian inference about the pseudo‐true parameter value is, in general, of lower asymptotic frequentist risk when the original posterior is substituted by an artificial normal posterior centered at the MLE with sandwich covariance matrix. An algorithm is suggested that allows the implementation of this artificial posterior also in models with high dimensional nuisance parameters which cannot reasonably be estimated by maximizing the likelihood.
Publication Date: 18-Sep-2013
Citation: Mueller, Ulrich K. (2013). RISK OF BAYESIAN INFERENCE IN MISSPECIFIED MODELS, AND THE SANDWICH COVARIANCE MATRIX. ECONOMETRICA, 81 (5), 1805 - 1849. doi:10.3982/ECTA9097
DOI: doi:10.3982/ECTA9097
ISSN: 0012-9682
EISSN: 1468-0262
Pages: 1805 - 1849
Type of Material: Journal Article
Journal/Proceeding Title: ECONOMETRICA
Version: Final published version. Article is made available in OAR by the publisher's permission or policy.



Items in OAR@Princeton are protected by copyright, with all rights reserved, unless otherwise indicated.