Identifying demand parameters in the presence of unobservables: A combined revealed and stated preference approach

We develop a combined, revealed and stated preference approach to identify discrete choice demand parameters in the presence of unobserved determinants of choice. Our approach overcomes difficulties associated with small choice sets, multicollinearity, and endogeneity that arise with revealed preference approaches. To illustrate our framework, we revisit two Canadian moose hunting datasets. Our empirical results suggest the potential gains from fusing revealed and stated preference data, but they also suggest its limitations when the data-generating processes for the data sources differ.