the approximate solution of finite‐horizon discrete‐choice dynamic programming models (replication data)

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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Suggested Citation

Eisenhauer, Philipp (2019): The approximate solution of finite‐horizon discrete‐choice dynamic programming models (replication data). Version: 1. Journal of Applied Econometrics. Dataset. http://dx.doi.org/10.15456/jae.2022327.0707254838