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Multivariate count data regression models with individual panel data from an on-site sample


  • Egan, Kevin
  • Herriges, Joseph


The purpose of this paper is to consider the problem of controlling for on-site sampling in the context of a system (or panel) of demand equations. Specifically, in the context of recreation demand, we are concerned with the situation in which survey respondents are asked to provide information not only about the actual trips to a specific site (observed behavior), but also their anticipated trips (either under current conditions or given price and quality changes). A Multivariate Poisson-log normal (MPLN) model and a seemingly unrelated negative binomial (SUNB) model are used to jointly model the observed and contingent behavior data and to correct for on-site sampling.
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  • Egan, Kevin & Herriges, Joseph, 2006. "Multivariate count data regression models with individual panel data from an on-site sample," Journal of Environmental Economics and Management, Elsevier, vol. 52(2), pages 567-581, September.
  • Handle: RePEc:eee:jeeman:v:52:y:2006:i:2:p:567-581

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    References listed on IDEAS

    1. Englin, Jeffrey & Shonkwiler, J S, 1995. "Estimating Social Welfare Using Count Data Models: An Application to Long-Run Recreation Demand under Conditions of Endogenous Stratification and Truncation," The Review of Economics and Statistics, MIT Press, vol. 77(1), pages 104-112, February.
    2. Murat K. Munkin & Pravin K. Trivedi, 1999. "Simulated maximum likelihood estimation of multivariate mixed-Poisson regression models, with application," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 29-48.
    3. Christopher D. Azevedo & Joseph A. Herriges & Catherine L. Kling, 2003. "Combining Revealed and Stated Preferences: Consistency Tests and Their Interpretations," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(3), pages 525-537.
    4. Klaus Moeltner & J. Scott Shonkwiler, 2005. "Correcting for On-Site Sampling in Random Utility Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 327-339.
    5. Laitila, Thomas, 1999. "Estimation of combined site-choice and trip-frequency models of recreational demand using choice-based and on-site samples," Economics Letters, Elsevier, vol. 64(1), pages 17-23, July.
    6. Jeffrey Englin & Trudy Cameron, 1996. "Augmenting travel cost models with contingent behavior data," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 7(2), pages 133-147, March.
    7. W. Douglass Shaw, 2002. "Testing the Validity of Contingent Behavior Trip Responses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 401-414.
    8. Grogger, J T & Carson, Richard T, 1991. "Models for Truncated Counts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(3), pages 225-238, July-Sept.
    9. Shaw, Daigee, 1988. "On-site samples' regression : Problems of non-negative integers, truncation, and endogenous stratification," Journal of Econometrics, Elsevier, vol. 37(2), pages 211-223, February.
    10. Whitehead, John C. & Haab, Timothy C. & Huang, Ju-Chin, 2000. "Measuring recreation benefits of quality improvements with revealed and stated behavior data," Resource and Energy Economics, Elsevier, vol. 22(4), pages 339-354, October.
    11. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
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