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Estimating Demand Systems when Outcomes Are Correlated Count

Author

Listed:
  • Herriges, Joseph A.
  • Phaneuf, Daniel J.
  • Tobias, Justin

Abstract

We develop a Bayesian posterior simulator for fitting a high dimensional system of ordinal or count outcome equations, illustrating its use by modeling the multiple site recreation demands of individual agents to a set of twenty-nine Iowa lakes. The model flexibly adjusts to match observed frequencies in trip outcomes, permits a flexible correlation pattern among the visited sites, and the posterior simulator for fitting this model is relatively easy to implement. We also describe how the model can be used to conduct counterfactual experiments, including predicting behavioral changes and describing welfare implications resulting from shifts in demographic and site characteristics.

Suggested Citation

  • Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin, 2008. "Estimating Demand Systems when Outcomes Are Correlated Count," Staff General Research Papers Archive 12934, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genres:12934
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    Cited by:

    1. Chandra R. Bhat & Rajesh Paleti & Palvinder Singh, 2014. "A Spatial Multivariate Count Model For Firm Location Decisions," Journal of Regional Science, Wiley Blackwell, vol. 54(3), pages 462-502, June.
    2. Ferdous, Nazneen & Eluru, Naveen & Bhat, Chandra R. & Meloni, Italo, 2010. "A multivariate ordered-response model system for adults' weekday activity episode generation by activity purpose and social context," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 922-943, September.
    3. Xie, Lusi & Adamowicz, Wiktor & Lloyd-Smith, Patrick, 2023. "Spatial and temporal responses to incentives: An application to wildlife disease management," Journal of Environmental Economics and Management, Elsevier, vol. 117(C).
    4. Carson, Richard T. & Eagle, Thomas C. & Islam, Towhidul & Louviere, Jordan J., 2022. "Volumetric choice experiments (VCEs)," Journal of choice modelling, Elsevier, vol. 42(C).
    5. Bhattacharjee, Subhra & Kling, Catherine L. & Herriges, Joseph A., 2009. "Kuhn-Tucker Estimation of Recreation Demand – A Study of Temporal Stability," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49408, Agricultural and Applied Economics Association.
    6. Nobel, Anne & Lizin, Sebastien & Witters, Nele & Rineau, Francois & Malina, Robert, 2020. "The impact of wildfires on the recreational value of heathland: A discrete factor approach with adjustment for on-site sampling," Journal of Environmental Economics and Management, Elsevier, vol. 101(C).
    7. Smith, Michael S. & Kauermann, Göran, 2011. "Bicycle commuting in Melbourne during the 2000s energy crisis: A semiparametric analysis of intraday volumes," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1846-1862.

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    Keywords

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    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General

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