TY - JOUR
T1 - Identification and estimation of a triangular model with multiple endogenous variables and insufficiently many instrumental variables
AU - Huang, Liquan
AU - Khalil, Umair
AU - Yıldız, Neşe
PY - 2019/2
Y1 - 2019/2
N2 - We develop a novel identification method for a partially linear model with multiple endogenous variables of interest but a single instrumental variable, which could even be binary. We present an easy-to-implement consistent estimator for the parametric part. This estimator retains n-convergence rate and asymptotic normality even though we have a generated regressor in our setup. The nonparametric part of the model is also identified. We also outline how our identification strategy can be extended to a fully non-parametric model. Finally, we use our methods to assess the impact of smoking during pregnancy on birth weight.
AB - We develop a novel identification method for a partially linear model with multiple endogenous variables of interest but a single instrumental variable, which could even be binary. We present an easy-to-implement consistent estimator for the parametric part. This estimator retains n-convergence rate and asymptotic normality even though we have a generated regressor in our setup. The nonparametric part of the model is also identified. We also outline how our identification strategy can be extended to a fully non-parametric model. Finally, we use our methods to assess the impact of smoking during pregnancy on birth weight.
KW - Control function approach
KW - Identification
KW - Multiple endogenous variables
UR - http://www.scopus.com/inward/record.url?scp=85057266693&partnerID=8YFLogxK
U2 - 10.1016/j.jeconom.2017.10.009
DO - 10.1016/j.jeconom.2017.10.009
M3 - Article
AN - SCOPUS:85057266693
VL - 208
SP - 346
EP - 366
JO - Journal of Econometrics
JF - Journal of Econometrics
SN - 0304-4076
IS - 2
ER -