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The approximate solution of finite‐horizon discrete‐choice dynamic programming models

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  • Philipp Eisenhauer

Abstract

The estimation of finite‐horizon discrete‐choice dynamic programming (DCDP) models is computationally expensive. This limits their realism and impedes verification and validation efforts. Keane and Wolpin (Review of Economics and Statistics, 1994, 76(4), 648–672) propose an interpolation method that ameliorates the computational burden but introduces approximation error. I describe their approach in detail, successfully recompute their original quality diagnostics, and provide some additional insights that underscore the trade‐off between computation time and the accuracy of estimation results.

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  • Philipp Eisenhauer, 2019. "The approximate solution of finite‐horizon discrete‐choice dynamic programming models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 149-154, January.
    • Handle: RePEc:wly:japmet:v:34:y:2019:i:1:p:149-154
    DOI: 10.1002/jae.2648
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    Cited by:

    1. Jack Britton & Ben Waltmann, 2021. "Revisiting the solution of dynamic discrete choice models: time to bring back Keane and Wolpin (1994)?," IFS Working Papers W21/13, Institute for Fiscal Studies.

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