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Generalized Indirect Inference for Discrete Choice Models

Author

Listed:
  • Marianne Bruins

    (Nuffield College and Dept of Economics, Univesity of Oxford)

  • James A. Duffy

    (Nuffield College, Dept of Economics and Institute for New Economic Thinking at the Oxford Martin School, Univesity of Oxford)

  • Michael P. Keane

    (Nuffield College and Dept of Economics, Univesity of Oxford)

  • Anthony A. Smith, Jr

    (Yale University)

Abstract

This paper develops and implements a practical simulation-based method for estimating dynamic discrete choice models. The method, which can accommodate lagged dependent variables, serially correlated errors, unobserved variables, and many alternatives, builds on the ideas of indirect inference. The main difficulty in implementing indirect inference in discrete choice models is that the objective surface is a step function, rendering gradientbased optimization methods useless. To overcome this obstacle, this paper shows how to smooth the objective surface. The key idea is to use a smoothed function of the latent utilities as the dependent variable in the auxiliary model. As the smoothing parameter goes to zero, this function delivers the discrete choice implied by the latent utilities, thereby guaranteeing consistency. We establish conditions on the smoothing such that our estimator enjoys the same limiting distribution as the indirect inference estimator, while at the same time ensuring that the smoothing facilitates the convergence of gradient-based optimization methods. A set of Monte Carlo experiments shows that the method is fast, robust, and nearly as efficient as maximum likelihood when the auxiliary model is sufficiently rich.

Suggested Citation

  • Marianne Bruins & James A. Duffy & Michael P. Keane & Anthony A. Smith, Jr, 2015. "Generalized Indirect Inference for Discrete Choice Models," Economics Papers 2015-W08, Economics Group, Nuffield College, University of Oxford.
  • Handle: RePEc:nuf:econwp:1508
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    References listed on IDEAS

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