Non-asymptotic inference in instrumental variables estimation - Joel L. Horowitz (Centre for Microdata Methods and Practice (cemmap), 2017)
This paper presents a simple non-asymptotic method for carrying out inference in IV models. 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.
Keywords: C21; C26; 330; Weak instruments; normal approximation; finite-sample bounds