Networking Your Way to a Better Prediction: Effectively Modeling Contingent Valuation Survey Data
Jason Bergtold (),
Daniel Taylor and
No 22152, 2003 Annual meeting, July 27-30, Montreal, Canada from American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association)
The purpose of this paper is to empirically compare the out-of-sample predictive capabilities of artificial neural networks, logit and probit models using dichotmous choice contingent valuation survey data. The authors find that feed-forward backpropagation artificial neural networks perform relatively better than the binary logit and probit models with linear index functions. In addition, guidelines for modeling contingent valuation survey data and how to estimate median WTP using artificial neural networks are provided.
Keywords: Research; Methods/; Statistical; Methods (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:aaea03:22152
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