Asymptotic Properties of Empirical Quantile-Based Estimators

We consider inference for parameters of the form $\theta_0 = E[F_Y^{-1}\circ F_Z(X)]$ for some variables $X$, $Y$ and $Z$.

Such parameters appear, in particular, in the ``changes-in-changes'' model of \cite{AtheyImbens2006}.

We first establish that $\widehat{\theta}$, a plug-in estimator of $\theta_0$, is root-$n$ consistent and asymptotically normal under weaker conditions than those previously available, allowing in particular for unbounded variables.

Next, we propose a new estimator of the asymptotic variance of $\widehat{\theta}$ and show its consistency, also allowing for unbounded variables.

Monte Carlo simulations suggest that the conditions for root-$n$ consistency and asymptotic normality are, in some sense, minimal.

These simulations highlight that our variance estimator also leads to more accurate inference than some alternative approaches.