To refer to this page use:
|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.|
|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|
|Pages:||1805 - 1849|
|Type of Material:||Journal Article|
|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.