Boosting GMM With Many Instruments When Some Are Invalid And/Or Irrelevant
提出一种双重标准Boosting方法，从大量候选工具变量中筛选出有效且相关的变量，用于GMM估计，模拟和汽车需求实证显示其偏差和均方根误差更低，价格弹性估计更富弹性。
ABSTRACT When the endogenous variable is an unknown function of observable instruments, its conditional mean can be approximated using the sieve functions of observable instruments. We propose a novel instrument selection method, double‐criteria boosting (DB), that consistently selects only valid and relevant instruments from a large set of candidate instruments. In the Monte Carlo simulation, we compare generalized method of moments (GMM) using DB (DB‐GMM) with other estimation methods and demonstrate that DB‐GMM gives lower bias and root mean squared error. In the empirical application to the automobile demand, the DB‐GMM estimator is suggesting a more elastic estimate of the price elasticity of demand than the standard two‐stage least square estimator.