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

Curtis Eberwein, John C. Ham

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)168-171
Number of pages4
JournalEconomics Letters
Volume101
Issue number3
DOIs
StatePublished - Dec 2008

Keywords

  • Computation
  • Derivatives
  • Dynamic programming
  • Standard errors
  • Structural estimation

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

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