Multivariate count data regression models with individual panel data from an on-site sample

The purpose of this paper is to consider the problem of controlling for on-site sampling in the context of a system (or panel) of demand equations. Specifically, in the context of recreation demand, we are concerned with the situation in which survey respondents are asked to provide information not only about the actual trips to a specific site (observed behavior), but also their anticipated trips (either under current conditions or given price and quality changes). A Multivariate Poisson-log normal (MPLN) model and a seemingly unrelated negative binomial (SUNB) model are used to jointly model the observed and contingent behavior data and to correct for on-site sampling.