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Andriy Norets, Brown University

Econometrics

Thursday, March 28, 2024, 04:00pm - 05:30pm

"Locally Robust Efficient Bayesian Inference"

Abstract: We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in a more general semiparametric model and the baseline model is only locally misspecified. In general, Bayesian and maximum likelihood estimators will be biased in these settings.  We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing semiparametric model, and an auxiliary parameter that has the same dimension as the  parameter of interest. We show that this augmentation asymptotically results in a marginal posterior for the parameter of interest that is normal with the mean equal to the semiparametrically efficient estimator and the  variance equal to the semiparametric efficiency bound. The augmented model nests the baseline model as a special case when the auxiliary parameter is zero. The approach should be especially useful when not only the parameters but other aspects of the distribution are of interest. We develop an MCMC algorithm for the augmented model estimation.  The approach is illustrated in applications.

4:00pm-5:30pm | In Person, 3rd Floor Library, NJ Hall | Coordinators: Ruonan Xu and Xiye Yang

Location  In Person, 3rd Floor Library, NJ Hall
Contact  Ruonan Xu and Xiye Yang