Another Look At The Estimation Of Dynamic Programming Models With Censored Decision Variables
In this paper we propose a new approach to estimate the structural parameters in the context of a censored continuous decision model. Instead of handling with the original model, we consider an approximate model in which the decision variable has been discretized in a finite number of values. In this sense, an ordered choice model becomes a natural approximation to an underlying and more complicated censored continuous one. We extend the kind of Hotz-Miller estimators proposed for the estimation of binary or multinomial choice models to the context of ordered choice models. The estimation approach is based on the existence of a one-to-one mapping from conditional choice value functions to conditional choice probabilities. Exploiting the invertibility of that mapping it is possible to obtain structural parameter estimates without solving the dynamic programming problem.
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