A0586
Title: Estimation based on martingale difference divergence with insufficient instrumental variables
Authors: Kunyang Song - The University of Hong Kong (Hong Kong) [presenting]
Abstract: Finding valid instrumental variables (IVs) is important but hard in the linear regression model. However, the classical estimation method such as 2-stage least square estimator is not applicable when the linear regression model is under-identified without enough valid IVs. Based on the martingale difference divergence (MDD), a new estimator is proposed for the general univariate nonlinear regression model, and this estimator is applicable even when the regression model is under-identified. Under certain regular conditions, the consistency and asymptotic normality of this MDD-based estimator is established. As an extension, a similar MDD-based estimator is also proposed for the multivariate nonlinear regression model. Simulations are given to illustrate the importance of the proposed estimators.
