Gmm and Empirical Likelihood with Incomplete Data

In applied work economists often encounter data generating mechanisms that produce censored or truncated observations. These dgp’s induce a probability distribution on the realized observations that differs from the underlying distribution for which inference is to be made. If this dichotomy between the target and realized populations is not taken into account, statistical inference can be severely biased. In this paper, we show how to do efficient semiparametric inference in moment condition models by supplementing the incomplete observations with some additional data that is not subject to censoring or truncation.