A Simple and Robust Estimator for Linear Regression Models with Strictly Exogenous Instruments

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