A Semi-Parametric Estimator for Revealed and Stated Preference Data

We present a semi-parametric approach for jointly estimating revealed and stated preference recreation demand models. The discrete factor method (DFM) allows for correlation across revealed preference and stated preference and incorporates unobserved heterogeneity into the conditional expectation of recreation demand. Our model is a generalized negative binomial with random effects, in which the random effect is composed of a discrete representation of unobserved heterogeneity and a rescaling coefficient that translates the heterogeneity measure into a demand effect. Our empirical application is to beach recreation demand in North Carolina. Statistical evidence supports our DFM specification, which imposes less restriction on model dispersion and incorporates unobserved heterogeneity in a more flexible manner. We find more predictive accuracy and smaller consumer surplus estimate with the DFM method in comparison with the standard random effects negative binomial. The structure of DFM allows for estimation of welfare by unobserved heterogeneity type