  In this paper we propose a new method of estimation for discrete choice
demand models when individual level data are available. The method employs a
two-step procedure. Step 1 predicts the choice probabilities as functions of
the observed individual level characteristics. Step 2 estimates the structural
parameters of the model using the estimated choice probabilities at a
particular point of interest and the moment restrictions. In essence, the
method uses nonparametric approximation (followed by) moment estimation. Hence
the name---NAME. We use simulations to compare the performance of NAME with the
standard methodology. We find that our method improves precision as well as
convergence time. We supplement the analysis by providing the large sample
properties of the proposed estimator.
