Identification and estimation of a triangular model with multiple endogenous variables and insufficiently many instrumental variables

Liquan Huang, Umair Khalil, Neşe Yıldız

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2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)346-366
Number of pages21
JournalJournal of Econometrics
Issue number2
Publication statusPublished - Feb 2019


  • Control function approach
  • Identification
  • Multiple endogenous variables

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