Implementing valid two-step identification-robust confidence sets for linear instrumental-variables models

In this article, we consider inference in the linear instrumental- variables models with one or more endogenous variables and potentially weak instruments. I developed a command, twostepweakiv, to implement the two- step identification-robust confidence sets proposed by Andrews (2018, Review of Economics and Statistics 100: 337–348) based on Wald tests and linear combi- nation tests (Andrews, 2016, Econometrica 84: 2155–2182). Unlike popular pro- cedures based on first-stage F statistics (Stock and Yogo, 2005, Testing for weak instruments in linear IV regression, in Identification and Inference for Economet- ric Models: Essays in Honor of Thomas Rothenberg), the two-step identification- robust confidence sets control coverage distortion without assuming the data are homoskedastic. I demonstrate the use of twostepweakiv with an example of an- alyzing the effect of wages on married female labor supply. For inference on sub- sets of parameters, twostepweakiv also implements the refined projection method (Chaudhuri and Zivot, 2011, Journal of Econometrics 164: 239–251). I illustrate that this method is more powerful than the conventional projection method using Monte Carlo simulations.