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Dynamic Random Utility Modeling: A Monte Carlo Analysis

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  • Robert L. Hicks
  • Kurt E. Schnier

Abstract

Applied studies of commercial fishing have largely ignored the intertemporal aspects of repeated site choices. For many fisheries, fishermen might choose a dynamically optimal cruise trajectory rather than myopic day-to-day strategies and a model that ignores these considerations will likely lead to biased parameter estimates and poor policy guidance. A dynamic random utility model is developed that utilizes the same information as static site-choice models but is entrenched in the principles of dynamic optimization. Using Monte Carlo analysis, we evaluate the performance of this estimator as compared to the static model for a variety of simulated fishery types. Copyright 2006, Oxford University Press.

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File URL: http://hdl.handle.net/10.1111/j.1467-8276.2006.00900.x
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Bibliographic Info

Article provided by Agricultural and Applied Economics Association in its journal American Journal of Agricultural Economics.

Volume (Year): 88 (2006)
Issue (Month): 4 ()
Pages: 816-835

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Handle: RePEc:oup:ajagec:v:88:y:2006:i:4:p:816-835

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Cited by:
  1. Hicks, Robert L. & Schnier, Kurt E., 2008. "Eco-labeling and dolphin avoidance: A dynamic model of tuna fishing in the Eastern Tropical Pacific," Journal of Environmental Economics and Management, Elsevier, vol. 56(2), pages 103-116, September.
  2. Hicks, Robert L. & Schnier, Kurt E., 2010. "Spatial regulations and endogenous consideration sets in fisheries," Resource and Energy Economics, Elsevier, vol. 32(2), pages 117-134, April.
  3. Chen, Min & Lupi, Frank, 2009. "Does economic endogeneity of site facilities in recreation demand models lead to statistical endogeneity?," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49449, Agricultural and Applied Economics Association.
  4. Junjie Zhang & Martin Smith, 2011. "Heterogeneous Response to Marine Reserve Formation: A Sorting Model approach," Environmental & Resource Economics, European Association of Environmental and Resource Economists, vol. 49(3), pages 311-325, July.
  5. Stafford, Tess, 2012. "Ignoring the Multi-species Aspect of Labor Supply Decisions in Spatially Explicit Bio-economic Fishery Models," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124367, Agricultural and Applied Economics Association.
  6. Paudel, Krishna P. & Caffey, Rex H. & Devkota, Nirmala, 2011. "An Evaluation of Factors Affecting the Choice of Coastal Recreational Activities," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 43(02), May.
  7. Robert L. Hicks & Kurt Schnier, 2006. "A Spatial Model of Dolphin Avoidance in the Eastern Tropical Pacific Ocean," Working Papers 25, Department of Economics, College of William and Mary.
  8. Shi Zheng & Pei Xu & Zhigang Wang, 2012. "Farmers' adoption of new plant varieties under variety property right protection: Evidence from rural China," China Agricultural Economic Review, Emerald Group Publishing, vol. 4(1), pages 124-140, February.
  9. C. Haynie, Alan & F. Layton, David, 2010. "An expected profit model for monetizing fishing location choices," Journal of Environmental Economics and Management, Elsevier, vol. 59(2), pages 165-176, March.
  10. Abbott, Joshua K. & Wilen, James E., 2011. "Dissecting the tragedy: A spatial model of behavior in the commons," Journal of Environmental Economics and Management, Elsevier, vol. 62(3), pages 386-401.

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