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Maximum Simulated Likelihood: A Brief Introduction For Practitioners

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  • Arias, Carlos
  • Cox, Thomas L.

Abstract

This paper discusses the increasing importance of probability simulation methods in the context of Maximum Simulated Likelihood. Three probability simulators are analyzed following their chronological order of appearance. This analysis provides an intuitive approach to the basic idea behind probability simulation, the successive improvements and probable future developments. The paper pays special attention to the role of simulation noise in Maximum Simulated Likelihood.

Suggested Citation

  • Arias, Carlos & Cox, Thomas L., 1999. "Maximum Simulated Likelihood: A Brief Introduction For Practitioners," Staff Papers 12662, University of Wisconsin-Madison, Department of Agricultural and Applied Economics.
    • Handle: RePEc:ags:wisagr:12662
    DOI: 10.22004/ag.econ.12662
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    References listed on IDEAS

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    Cited by:

    1. Vincenzo Atella & Carlos Arias & Federico Perali & Raffaella Castagnini, 2003. "Estimation of the Sharing Rule Between Adults and Children and Related Equivalence Scales Within a Collective Consumption Framework," CEIS Research Paper 28, Tor Vergata University, CEIS.
    2. Henry W. Chappell & Paulo Guimarães & Orgül Demet Öztürk, 2011. "Confessions of an internet monopolist: demand estimation for a versioned information good," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 32(1), pages 1-15, January.
    3. Ozturk, Erdogan & Irwin, Elena G., 2001. "Explaining Household Location Choices Using A Spatial Probit Model," 2001 Annual meeting, August 5-8, Chicago, IL 20626, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Kyösti Pietola & Alfons Oude Lansink, 2006. "Energy‐saving Technology Choices by Dutch Glasshouse Firms," Journal of Agricultural Economics, Wiley Blackwell, vol. 57(1), pages 129-144, March.
    5. Michael J. Peel, 2014. "Addressing unobserved endogeneity bias in accounting studies: control and sensitivity methods by variable type," Accounting and Business Research, Taylor & Francis Journals, vol. 44(5), pages 545-571, October.

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