csa2sls: A complete subset approach for many instruments using Stata

We developed a command, csa2sls, that implements the complete subset averaging two-stage least-squares (CSA2SLS) estimator in Lee and Shin (2021, Econometrics Journal 24: 290–314). The CSA2SLS estimator is an alternative to the two-stage least-squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that the CSA2SLS estimator reduces both the mean squared error and the estimation bias substantially when instruments are correlated. We illustrate the usage of csa2sls in Stata with an empirical application.

我们开发了命令csa2sls，以实现Lee和Shin（2021，《计量经济学杂志》24：290–314）提出的完全子集平均两阶段最小二乘法（CSA2SLS）估计量。CSA2SLS估计量是两阶段最小二乘法估计量的替代方案，可纠正由大量相关工具变量导致的偏差问题。我们通过蒙特卡洛模拟证实，当工具变量存在相关性时，CSA2SLS估计量能显著降低均方误差与估计偏差。我们还通过实证应用案例说明了csa2sls命令在Stata软件中的使用方法。