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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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    1. Peter Arcidiacono & Holger Sieg & Frank Sloan, 2007. "Living Rationally Under The Volcano? An Empirical Analysis Of Heavy Drinking And Smoking," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(1), pages 37-65, February.
    2. 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:

    1. Mai, Tien & Frejinger, Emma & Fosgerau, Mogens & Bastin, Fabian, 2017. "A dynamic programming approach for quickly estimating large network-based MEV models," Transportation Research Part B: Methodological, Elsevier, vol. 98(C), pages 179-197.
    2. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    3. Mai, Tien & Frejinger, Emma & Bastin, Fabian, 2015. "A misspecification test for logit based route choice models," Economics of Transportation, Elsevier, vol. 4(4), pages 215-226.

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