csa2sls: A complete subset approach for many instruments using Stata

TL;DR

This paper introduces the csa2sls command in Stata, implementing the CSA2SLS estimator to address bias issues in models with many correlated instruments, demonstrated through simulations and an empirical example.

Contribution

The paper develops a new Stata command for the CSA2SLS estimator, providing an effective alternative to traditional two-stage least squares with bias correction in many-instrument settings.

Findings

CSA2SLS reduces mean squared error in simulations

CSA2SLS decreases bias with correlated instruments

Empirical application demonstrates practical utility

Abstract

We develop a Stata command $\texttt{csa2sls}$ that implements the complete subset averaging two-stage least squares (CSA2SLS) estimator in Lee and Shin (2021). 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 $\texttt{csa2sls}$ in Stata by an empirical application.