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

Juan Carlos Escanciano

No 2017-001, CAEPR Working Papers from Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington

Abstract: This paper investigates estimation of linear regression models with strictly exogenous instruments under minimal identifying assumptions. The paper introduces auniformly (in the data generating process) consistent estimator under nearly minimalidentifying 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.

Keywords: Uniform identification; Instrumental variables; Weak instruments; Uniform inference; Intertemporal elasticity of substitution (search for similar items in EconPapers)
JEL-codes: C13 C26 (search for similar items in EconPapers)
Pages: 31 pages
Date: 2016-11
New Economics Papers: this item is included in nep-ecm
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https://caepr.indiana.edu/RePEc/inu/caeprp/caepr2017-001.pdf (application/pdf)

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Journal Article: A simple and robust estimator for linear regression models with strictly exogenous instruments (2018) Downloads
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