Journal article
Journal of Environmental Economics and Management, vol. 35, 1998, pp. 262-276
APA
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Morey, E. R., & Waldman, D. (1998). Measurement error in recreation demand models: the joint estimation of participation, site choice, and site characteristics. Journal of Environmental Economics and Management, 35, 262–276. https://doi.org/https://www.sciencedirect.com/science/article/abs/pii/S0095069698910294?via%3Dihub
Chicago/Turabian
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Morey, Edward R., and Donald Waldman. “Measurement Error in Recreation Demand Models: the Joint Estimation of Participation, Site Choice, and Site Characteristics.” Journal of Environmental Economics and Management 35 (1998): 262–276.
MLA
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Morey, Edward R., and Donald Waldman. “Measurement Error in Recreation Demand Models: the Joint Estimation of Participation, Site Choice, and Site Characteristics.” Journal of Environmental Economics and Management, vol. 35, 1998, pp. 262–76, doi:https://www.sciencedirect.com/science/article/abs/pii/S0095069698910294?via%3Dihub.
BibTeX Click to copy
@article{edward1998a,
title = {Measurement error in recreation demand models: the joint estimation of participation, site choice, and site characteristics.},
year = {1998},
journal = {Journal of Environmental Economics and Management},
pages = {262-276},
volume = {35},
doi = {https://www.sciencedirect.com/science/article/abs/pii/S0095069698910294?via%3Dihub},
author = {Morey, Edward R. and Waldman, Donald}
}
Abstract In the demand for recreational fishing sites, an important explanatory variable differentiating sites is the unobserved expected catch rate. Since the observed catch rate is subject to sampling variability, using the average of a site's observed catch rates causes the parameter estimator on catch to be biased downward. We develop and demonstrate a solution to this errors-in-variables problem when there are repeated measurements on the catch rate. Consistent and efficient estimates of both the demand parameters and the expected catch rates are obtained by simultaneously estimating them by maximum likelihood. An empirical example demonstrates the importance of simultaneous estimation.