A simple and robust estimator for linear regression models with strictly exogenous instruments

Juan Carlos Escanciano

Econometrics Journal, 2018, vol. 21, issue 1, 36-54

Abstract: In 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.

Date: 2018
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Citations: View citations in EconPapers (10)

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https://doi.org/10.1111/ectj.12087

Related works:
Working Paper: A Simple and Robust Estimator for Linear Regression Models with Strictly Exogenous Instruments (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:wly:emjrnl:v:21:y:2018:i:1:p:36-54

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