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

Philipp Eisenhauer

Journal of Applied Econometrics, 2019, vol. 34, issue 1, 149-154

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.

Date: 2019
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