cemmap working paper, Centre for Microdata Methods and Practice CWP39/12
In this paper we study nonparametric estimation in a binary treatment model where the outcome equation is of unrestricted form, and the selection equation contains multiple unobservables that enter through a nonparametric random coefficients specification. This specification is flexible because it allows for complex unobserved heterogeneity of economic agents and non-monotone selection into treatment. We obtain conditions under which both the conditional distributions of Y0 and Y1, the outcome for the untreated, respectively treated, given first stage unobserved random coefficients, are identified. We can thus identify an average treatment effect, conditional on first stage unobservables called UCATE, which yields most treatment effects parameters that depend on averages, like ATE and TT. We provide sharp bounds on the variance, the joint distribution of (Y0, Y1) and the distribution of treatment effects. In the particular case where the outcomes are continuously distributed, we provide novel and weak conditions that allow to point identify the joint conditional distribution of Y0, Y1, given the unobservables. This allows to derive every treatment effect parameter, e.g. the distribution of treatment effects and the proportion of individuals who benefit from treatment. We present estimators for the marginals, average and distribution of treatment effects, both conditional on unobservables and unconditional, as well as total population effects. The estimators use all the data and discard tail values of the instruments when they are too unlikely. We provide their rates of convergence, and analyze their finite sample behavior in a simulation study. Finally, we also discuss the situation where some of the instruments are discrete.
Treatment Effects Endogeneity Random Coefficients Nonparametric Identification Partial Identification Roy Model Ill-Posed Inverse Problems Deconvolution Radon Transform Rates of Convergence