This paper presents a simple method for carrying out inference in a wide
variety of possibly nonlinear IV models under weak assumptions. The method is
non-asymptotic in the sense that it provides a finite sample bound on the
difference between the true and nominal probabilities of rejecting a correct
null hypothesis. The method is a non-Studentized version of the Anderson-Rubin
test but is motivated and analyzed differently. In contrast to the conventional
Anderson-Rubin test, the method proposed here does not require restrictive
distributional assumptions, linearity of the estimated model, or simultaneous
equations. Nor does it require knowledge of whether the instruments are strong
or weak. It does not require testing or estimating the strength of the
instruments. The method can be applied to quantile IV models that may be
nonlinear and can be used to test a parametric IV model against a nonparametric
alternative. The results presented here hold in finite samples, regardless of
the strength of the instruments.Comment: 33 pages, 5 table
