English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We provide a comprehensive semi-parametric study of Bayesian partiallyidentified econometric models. While the existing literature on Bayesianpartial identification has mostly focused on the structural parameter, ourprimary focus is on Bayesian credible sets (BCS's) of the unknown identifiedset and the posterior distribution of its support function. We construct a(two-sided) BCS based on the support function of the identified set. We provethe Bernstein-von Mises theorem for the posterior distribution of the supportfunction. This powerful result in turn infers that, while the BCS and thefrequentist confidence set for the partially identified parameter areasymptotically different, our constructed BCS for the identified set has anasymptotically correct frequentist coverage probability. Importantly, weillustrate that the constructed BCS for the identified set does not require aprior on the structural parameter. It can be computed efficiently for subsetinference, especially when the target of interest is a sub-vector of thepartially identified parameter, where projecting to a low-dimensional subset isoften required. Hence, the proposed methods are useful in many applications.  The Bayesian partial identification literature has been assuming a knownparametric likelihood function. However, econometric models usually onlyidentify a set of moment inequalities, and therefore using an incorrectlikelihood function may result in misleading inferences. In contrast, with anonparametric prior on the unknown likelihood function, our proposed Bayesianprocedure only requires a set of moment conditions, and can efficiently makeinference about both the partially identified parameter and its identified set.This makes it widely applicable in general moment inequality models. Finally,the proposed method is illustrated in a financial asset pricing problem.

Semi-parametric Bayesian Partially Identified Models based on Support Function