We consider the problem of inference on a class of sets describing a collection of admissible models as solutions to a … parameters, making them especially appealing in the target applications. We show that the resulting inference procedures are more …

conditional likelihood ratio test confidence set identification inference moment conditions robust singular variance subvector …

Indirect Inference (I‐I) estimation of structural parameters θ requires matching observed and simulated statistics … instrumental parameters will encapsulate the statistical information used for inference about the structural parameters. As such …

of factor jumps. Such jump dependence is implied by standard linear factor models. Our inference is based on a panel of …

This paper considers issues related to identification, inference, and computation in linearized dynamic stochastic … quasi-Bayesian procedure for estimation and inference. The procedure can be used to incorporate relevant prior distributions …

This paper develops a Bayesian approach to inference in a class of partially identified econometric models. Models in … for a partially identified parameter θ . The approach maps posterior inference about μ to various posterior inference … statements concerning the identified set for μ , without the specification of a prior for θ . Many posterior inference statements …

an aid rather than a hindrance to identification. We present a nonparametric estimation and inference approach relying on …