This paper investigates estimation of linear regression models with strictly exogenous instruments under minimal identifying assumptions. The paper introduces a uniformly (in the data generating process) consistent estimator under nearly minimal identifying assumptions. The proposed estimator, called the Integrated Instrumental Variables (IIV) estimator, is a simple weighted least squares estimator and does not require the choice of a bandwidth or tuning parameter, or the selection of a finite set of instruments. Thus, the estimator is extremely simple to implement. Monte Carlo evidence supports the theoretical claims and suggests that the IIV estimator is a robust complement to optimal IV in finite samples. In an application with quarterly UK data, IIV estimates a positive and significant elasticity of intertemporal substitution and an equally sensible estimate for its reciprocal, in sharp contrast to IV methods that fail to identify these parameters.
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