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This paper develops and implements a practical simulation-based method forestimating dynamic discrete choice models. The method, which can accommodatelagged dependent variables, serially correlated errors, unobserved variables,and many alternatives, builds on the ideas of indirect inference. The maindifficulty in implementing indirect inference in discrete choice models is thatthe objective surface is a step function, rendering gradient-based optimizationmethods useless. To overcome this obstacle, this paper shows how to smooth theobjective surface. The key idea is to use a smoothed function of the latentutilities as the dependent variable in the auxiliary model. As the smoothingparameter goes to zero, this function delivers the discrete choice implied bythe latent utilities, thereby guaranteeing consistency. We establish conditionson the smoothing such that our estimator enjoys the same limiting distributionas the indirect inference estimator, while at the same time ensuring that thesmoothing facilitates the convergence of gradient-based optimization methods. Aset of Monte Carlo experiments shows that the method is fast, robust, andnearly as efficient as maximum likelihood when the auxiliary model issufficiently rich.

Generalized Indirect Inference for Discrete Choice Models