Dynamic Programming with Hermite Approximation

Yongyang Cai and Kenneth Judd

No 18540, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Numerical dynamic programming algorithms typically use Lagrange data to approximate value functions over continuous states. Hermite data is easily obtained from solving the Bellman equation and can be used to approximate value functions. We illustrate this method with one-, three-, and six-dimensional examples. We find that value function iteration with Hermite approximation improves accuracy by one to three digits using little extra computing time. Moreover, Hermite approximation is significantly faster than Lagrange for the same accuracy, and this advantage increases with dimension.

JEL-codes: C61 C63 (search for similar items in EconPapers)
Date: 2012-11
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Published as Mathematical Methods of Operations Research June 2015, Volume 81, Issue 3, pp 245-267 Date: 13 Feb 2015 Dynamic programming with Hermite approximation Yongyang Cai, Kenneth L. Judd

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Journal Article: Dynamic programming with Hermite approximation (2015)
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