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Obtaining analytic derivatives for a popular discrete-choice dynamic programming model

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  • Eberwein, Curtis
  • Ham, John C.

Abstract

We show how to recursively calculate analytic first and second derivatives of the likelihood for a popular discrete-choice, dynamic programming model. These allow for decreased computing time, and are useful for de-bugging complicated program code and accurately estimating standard errors.

Suggested Citation

  • Eberwein, Curtis & Ham, John C., 2008. "Obtaining analytic derivatives for a popular discrete-choice dynamic programming model," Economics Letters, Elsevier, vol. 101(3), pages 168-171, December.
    • Handle: RePEc:eee:ecolet:v:101:y:2008:i:3:p:168-171
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    References listed on IDEAS

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    4. Curtis Eberwein & John C. Ham & Robert J. Lalonde, 1997. "The Impact of Being Offered and Receiving Classroom Training on the Employment Histories of Disadvantaged Women: Evidence from Experimental Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(4), pages 655-682.
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    Cited by:

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    3. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.

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