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Standard and Shuffled Halton Sequences in a Mixed Logit Model

Author

Listed:
  • Alexander Staus

    (Institute for Agricultural Policy and Agricultural Markets, University of Hohenheim)

Abstract
Modeling consumer choice in different areas has lead to an increase use of discrete choice models. Probit or Multinomial Logit Models are often the base of further empirical research of consumer choice. In some of these models the equations to solve have no closed-form expression. They include multi-dimensional integrals which can not be solved analytically. Simulation methods have been developed to approximate a solution for these integrals. This paper describes the Standard Halton sequence and a modification of it, the Shuffled Halton sequence. Both are simulation methods which can reduce computational effort compared to a random sequence. We compare the simulation methods in their coverage of the multi-dimensional area and in their estimation results using data of consumer choice on grocery store formats.

Suggested Citation

  • Alexander Staus, 2008. "Standard and Shuffled Halton Sequences in a Mixed Logit Model," Hohenheimer Agrarökonomische Arbeitsberichte 17, University of Hohenheim, Institute for Agricultural Policy and Agricultural Markets.
  • Handle: RePEc:hoh:hoh420:17
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    References listed on IDEAS

    as
    1. Sándor, Zsolt & Train, Kenneth, 2004. "Quasi-random simulation of discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 38(4), pages 313-327, May.
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    simulation; mixed logit; halton sequence;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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