Before revision:  The first stage resembles that of sieve minimum-distance estimation, and the second stage requires numerical solution of a linear programming problem. In contrast to existing methods our second stage does not require that the researcher choose any penalty function, penalty parameter nor a particular dimension of a sieve space. To compare our method to existing approaches we present an empirical application based on Horowitz (2011)  .
After revision:  The first stage resembles that of sieve minimum-distance estimation, and the second stage is formulated as a linear program. We apply our methods to the empirical setting of Blundell et al. (2007) and Horowitz (2011) .