Binary Response Model With Many Weak Instruments
针对含许多弱工具变量的内生二元响应模型，采用控制函数和正则化方法，提出两种一致且渐近正态的估计量，蒙特卡洛模拟显示其优于现有方法，并用于研究家庭收入对大学完成的影响。
ABSTRACT This paper considers an endogenous binary response model with many weak instruments. We employ a control function approach and a regularization scheme to obtain better estimation results for the endogenous binary response model in the presence of many weak instruments. Two consistent and asymptotically normally distributed estimators are provided, each of which is called a regularized conditional maximum likelihood estimator (RCMLE) and a regularized nonlinear least squares estimator (RNLSE). Monte Carlo simulations show that the proposed estimators outperform the existing ones when there are many weak instruments. We use the proposed estimation method to examine the effect of family income on college completion.