RT Journal Article
T1 A simple and robust estimator for linear regression models with strictly exogenous instruments
A1 Escanciano, Juan Carlos
AB 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.
PB Oxford University Press
SN 1368-4221
YR 2018
FD 2018-02-01
LK https://hdl.handle.net/10016/35094
UL https://hdl.handle.net/10016/35094
LA eng
DS e-Archivo
RD 1 sept. 2024