Publication: A simple and robust estimator for linear regression models with strictly exogenous instruments
Loading...
Identifiers
Publication date
2018-02-01
Defense date
Authors
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford University Press
Impact
Export
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.
Description
Keywords
Uniform identification, Instrumental variables, Weak instruments, Uniform inference, Intertemporal Elasticity Of Substitution
Bibliographic citation
Escanciano, J. C. (2017). A simple and robust estimator for linear regression models with strictly exogenous instruments. The Econometrics Journal, 21 (1), pp. 36-54.