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Estimating a System of Recreation Demand Functions Using a Seemingly Unrelated Poisson Regression Approach

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  • Ozuna, Teofilo, Jr
  • Gomez, Irma Adriana

Abstract

In this article, a seemingly unrelated Poisson regression model is presented as an alternative to using Zellner's seemingly unrelated regression model for estimating a system of recreation demand functions. The seemingly unrelated Poisson regression model provides estimates that are asymptotically more efficient than equation-by-equation Poisson estimates and circumvents the bias and inconsistency problems that result when using A. Zellner's seemingly unrelated regression model. Additionally, the seemingly unrelated Poisson regression model is applied to an empirical problem dealing with the value of recreational boating and the findings indicate that the seemingly unrelated regression model consumer surplus estimates are substantially different from those of the seemingly unrelated Poisson regression model. Copyright 1994 by MIT Press.

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  • Ozuna, Teofilo, Jr & Gomez, Irma Adriana, 1994. "Estimating a System of Recreation Demand Functions Using a Seemingly Unrelated Poisson Regression Approach," The Review of Economics and Statistics, MIT Press, vol. 76(2), pages 356-360, May.
    • Handle: RePEc:tpr:restat:v:76:y:1994:i:2:p:356-60
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    Cited by:

    1. Hilger, James & Englin, Jeffrey, 2009. "Utility theoretic semi-logarithmic incomplete demand systems in a natural experiment: Forest fire impacts on recreational values and use," Resource and Energy Economics, Elsevier, vol. 31(4), pages 287-298, November.
    2. von Haefen, Roger H., 2010. "Incomplete Demand Systems, Corner Solutions, and Welfare Measurement," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 39(1), pages 1-15, February.
    3. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008. "Estimating demand systems when outcomes are correlated counts," Journal of Econometrics, Elsevier, vol. 147(2), pages 282-298, December.
    4. Hellström, Jörgen & Nordström, Jonas, 2012. "Demand and welfare effects in recreational travel models: Accounting for substitution between number of trips and days to stay," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(3), pages 446-456.
    5. Baek, Ji Won, 2016. "The effects of the Internet and mobile services on urban household expenditures: The case of South Korea," Telecommunications Policy, Elsevier, vol. 40(1), pages 22-38.
    6. 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).
    7. Phaneuf, Daniel J. & Smith, V. Kerry, 2006. "Recreation Demand Models," Handbook of Environmental Economics, in: K. G. Mäler & J. R. Vincent (ed.), Handbook of Environmental Economics, edition 1, volume 2, chapter 15, pages 671-761, Elsevier.
    8. von Haefen, Roger H. & Phaneuf, Daniel J., 2003. "Estimating preferences for outdoor recreation:: a comparison of continuous and count data demand system frameworks," Journal of Environmental Economics and Management, Elsevier, vol. 45(3), pages 612-630, May.

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