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An Information-Theoretic Approach to Estimating Willingness To Pay for River Recreation Site Attributes

Author

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  • Henry, Miguel
  • Mittelhammer, Ron
  • Loomis, John

Abstract

This study applies an information theoretic econometric approach in the form of a new maximum likelihood-minimum power divergence (ML-MPD) semi-parametric binary response estimator to analyze dichotomous contingent valuation data. The ML-MPD method estimates the underlying behavioral decision process leading to a person’s willingness to pay for river recreation site attributes. Empirical choice probabilities, willingness to pay measures for recreation site attributes, and marginal effects of changes in some explanatory variables are estimated. For comparison purposes, a Logit model is also implemented. A Wald test of the symmetric logistic distribution underlying the Logit model is rejected at the 0.01 level in favor of the ML-MPD distribution model. Moreover, based on several goodness-of-fit measures we find that the ML-MPD is superior to the Logit model. Our results also demonstrate the potential for substantially overstating the precision of the estimates and associated inferences when the imposition of unknown structural information is not accounted explicitly for in the model. The ML-MPD model provides more intuitively reasonable and defensible results regarding the valuation of river recreation than the Logit model.

Suggested Citation

  • Henry, Miguel & Mittelhammer, Ron & Loomis, John, 2018. "An Information-Theoretic Approach to Estimating Willingness To Pay for River Recreation Site Attributes," MPRA Paper 89842, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:89842
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    References listed on IDEAS

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    1. Crooker, John R. & Herriges, Joseph A., 2004. "Parametric and Semi-Nonparametric Estimation of Willingness-To-Pay in a Contingent Valuation Framework," Staff General Research Papers Archive 11156, Iowa State University, Department of Economics.
    2. Lee, Joanne & Cho, Wendy K. Tam & Judge, George G., 2010. "Stigler's approach to recovering the distribution of first significant digits in natural data sets," Statistics & Probability Letters, Elsevier, vol. 80(2), pages 82-88, January.
    3. Chen, Songnian & Khan, Shakeeb, 2003. "Rates of convergence for estimating regression coefficients in heteroskedastic discrete response models," Journal of Econometrics, Elsevier, vol. 117(2), pages 245-278, December.
    4. Chen, Heng Z. & Randall, Alan, 1997. "Semi-nonparametric estimation of binary response models with an application to natural resource valuation," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 323-340.
    5. Creel, Michael & Loomis, John, 1997. "Semi-nonparametric Distribution-Free Dichotomous Choice Contingent Valuation," Journal of Environmental Economics and Management, Elsevier, vol. 32(3), pages 341-358, March.
    6. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    7. Guido W. Imbens & Richard H. Spady & Phillip Johnson, 1998. "Information Theoretic Approaches to Inference in Moment Condition Models," Econometrica, Econometric Society, vol. 66(2), pages 333-358, March.
    8. Matzkin, Rosa L, 1992. "Nonparametric and Distribution-Free Estimation of the Binary Threshold Crossing and the Binary Choice Models," Econometrica, Econometric Society, vol. 60(2), pages 239-270, March.
    9. Timothy C. Haab & Kenneth E. McConnell, 2002. "Valuing Environmental and Natural Resources," Books, Edward Elgar Publishing, number 2427.
    10. W. Michael Hanemann, 1984. "Welfare Evaluations in Contingent Valuation Experiments with Discrete Responses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(3), pages 332-341.
    11. Huang, Ju-Chin & Nychka, Douglas W. & Smith, V. Kerry, 2008. "Semi-parametric discrete choice measures of willingness to pay," Economics Letters, Elsevier, vol. 101(1), pages 91-94, October.
    12. Daniel McFadden, 1994. "Contingent Valuation and Social Choice," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 76(4), pages 689-708.
    13. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    14. Klein, Roger W & Spady, Richard H, 1993. "An Efficient Semiparametric Estimator for Binary Response Models," Econometrica, Econometric Society, vol. 61(2), pages 387-421, March.
    15. Gregory L. Poe & Kelly L. Giraud & John B. Loomis, 2005. "Computational Methods for Measuring the Difference of Empirical Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 87(2), pages 353-365.
    16. Manski, Charles F., 1975. "Maximum score estimation of the stochastic utility model of choice," Journal of Econometrics, Elsevier, vol. 3(3), pages 205-228, August.
    17. Chuan-Zhong Li, 1996. "Semiparametric Estimation of the Binary Choice Model for Contingent Valuation," Land Economics, University of Wisconsin Press, vol. 72(4), pages 462-473.
    18. Joseph C. Cooper & Michael Hanemann & Giovanni Signorello, 2002. "One-and-One-Half-Bound Dichotomous Choice Contingent Valuation," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 742-750, November.
    19. Gonzalez, Juan Marcos & Loomis, John B. & Gonzalez-Caban, Armando, 2008. "A Joint Estimation Method to Combine Dichotomous Choice CVM Models with Count Data TCM Models Corrected for Truncation and Endogenous Stratification," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 40(2), pages 1-15, August.
    20. Judge,George G. & Mittelhammer,Ron C., 2012. "An Information Theoretic Approach to Econometrics," Cambridge Books, Cambridge University Press, number 9780521869591.
    21. Ernst R. Berndt & Bronwyn H. Hall & Robert E. Hall & Jerry A. Hausman, 1974. "Estimation and Inference in Nonlinear Structural Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 3, number 4, pages 653-665, National Bureau of Economic Research, Inc.
    22. González, Juan Marcos & Loomis, John B. & González-Cabán, Armando, 2008. "A Joint Estimation Method to Combine Dichotomous Choice CVM Models with Count Data TCM Models Corrected for Truncation and Endogenous Stratification," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 40(2), pages 681-695, August.
    23. John Crooker & Joseph Herriges, 2004. "Parametric and Semi-Nonparametric Estimation of Willingness-to-Pay in the Dichotomous Choice Contingent Valuation Framework," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 27(4), pages 451-480, April.
    24. Judge,George G. & Mittelhammer,Ron C., 2012. "An Information Theoretic Approach to Econometrics," Cambridge Books, Cambridge University Press, number 9780521689731.
    25. Joseph C. Cooper & Michael Hanemann & Giovanni Signorello, 2002. "One-and-One-Half-Bound Dichotomous Choice Contingent Valuation," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 742-750, November.
    26. Horowitz, Joel L, 1992. "A Smoothed Maximum Score Estimator for the Binary Response Model," Econometrica, Econometric Society, vol. 60(3), pages 505-531, May.
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    2. Miguel Henry & George Judge, 2019. "Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series," Econometrics, MDPI, vol. 7(1), pages 1-16, March.

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    More about this item

    Keywords

    Minimum power divergence; contingent valuation; binary response models; information theoretic econometrics; river recreation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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