Escanciano, Juan Carlos2022-06-132022-06-132018-02-01Escanciano, J. C. (2017). A simple and robust estimator for linear regression models with strictly exogenous instruments. The Econometrics Journal, 21 (1), pp. 36-54.1368-4221https://hdl.handle.net/10016/35094In this paper, I investigate the estimation of linear regression models with strictly exogenous instruments under minimal identifying assumptions. I introduce 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. It 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 instrumental variables in finite samples. In an application with quarterly UK data, the IIV estimator estimates a positive and significant elasticity of intertemporal substitution and an equally sensible estimate for its reciprocal, in sharp contrast to instrumental variables methods that fail to identify these parameters.eng© 2017 Royal Economic SocietyUniform identificationInstrumental variablesWeak instrumentsUniform inferenceIntertemporal Elasticity Of Substitutionresearch articleC13C26Economíahttps://doi.org/10.1111/ectj.12087open access36154Econometrics Journal21AR/0000029576