We develop a Bayesian semi-parametric approach to the instrumental variable problem. We assume linear structural and reduced form equations, but model the error distributions non-parametrically. A Dirichlet process prior is used for the joint distribution of structural and instrumental variable...

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to be any functions, including ones carrying an index, which can be...

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to carry an index, and to be unknown but parametrically or...

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identifi cation literature by allowing the upper and lower functions de ning the band to carry an index, and to be unknown but parametrically or...

This paper provides inference methods for best linear approximations to functions which are known to lie within a band. It extends the partial identification literature by allowing the upper and lower functions defining the band to carry an index, and to be unknown but parametrically or...

We provide a tractable characterization of the sharp identification region of the parameters ø in a broad class of incomplete econometric models. Models in this class have set valued predictions that yield a convex set of conditional or unconditional moments for the observable model variables....