Static modeling of dynamic recreation behavior: Implications for prediction and welfare estimation

  • Baerenklau K
  • Provencher B
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Abstract

This paper examines the consequences of using a static model of recreation trip-taking behavior when the underlying decision problem is dynamic. Specifically we examine the implications for trip forecasting and welfare estimation using a panel dataset of Lake Michigan salmon anglers for the 1996 and 1997 fishing seasons. We derive and estimate both a structural dynamic model using Bellman's equation, and a reduced-form static model with trip probability expressions mimicking those of the dynamic model. We illustrate an inherent identification problem in the reduced-form model that creates biased welfare estimates, and we discuss the general implications of this for the interpretation of preference parameters in static models. We then use both models to simulate trip taking behavior and show that although their in-sample trip forecasts are similar, their welfare estimates and out-of-sample forecasts are quite different. © 2005 Elsevier Inc. All rights reserved.

Author-supplied keywords

  • Bellman's equation
  • Dynamic programming
  • Dynamic structural estimation
  • Kuhn-Tucker
  • Logit
  • Mental accounting
  • Random utility model
  • Recreation benefits estimation
  • Reduced-form estimation
  • Rum

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Authors

  • Kenneth A. Baerenklau

  • Bill Provencher

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