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Adaptive Grids for the Estimation of Dynamic Models

Author

Listed:
  • Andreas Lanz

    (HEC Paris - Ecole des Hautes Etudes Commerciales)

  • Gregor Reich
  • Ole Wilms

Abstract

This paper develops a method to flexibly adapt interpolation grids of value function approximations in the estimation of dynamic models using either NFXP (Rust, 1987) or MPEC (Su and Judd, 2012). Since MPEC requires the grid structure for the value function approximation to be hard-coded into the constraints, one cannot apply iterative node insertion for grid refinement; for NFXP, grid adaption by (iteratively) inserting new grid nodes will generally lead to discontinuous likelihood functions. Therefore, we show how to continuously adapt the grid by moving the nodes, a technique referred to as r-adaption. We demonstrate how to obtain optimal grids based on the balanced error principle, and implement this approach by including additional constraints to the likelihood maximization problem. The method is applied to two models: (i) the bus engine replacement model (Rust, 1987), modified to feature a continuous mileage state, and (ii) to a dynamic model of content consumption using original data from SoundCloud, the largest user-generated content network in the music domain.

Suggested Citation

  • Andreas Lanz & Gregor Reich & Ole Wilms, 2020. "Adaptive Grids for the Estimation of Dynamic Models," Working Papers hal-02953037, HAL.
    • Handle: RePEc:hal:wpaper:hal-02953037
    DOI: 10.2139/ssrn.2650994
    as

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