An evaluation of the use of ordinary least squares for estimating tourism demand models

Estimating tourism demand models involves a set of related equations with errors that may not satisfy the common assumptions ofbeing independent, with constant variance and normal distribution. In such circumstances, seemingly unrelated regression estimation may be considered a better estimation technique than ordinary least squares. Results from a simulation exercise, however, show that generally there is little difference between ordi-nary least squares andseemingly unrelated regression. The ordinary least squares technique performs well, and the results give little reason to use more complex estimation techniques. Another feature oftourism data is that strong growth in tourist numbers is often observed. This feature implies that models in which such series are the dependent variable are not consistently estimated by least squares methods. A percentage error loss junction is proposed as a more appropriate criterion for estimating tourist data of this type. Modeling tourism flows to a destination usually involves segmenting the inbound market by the origins of the tourists (see Crouch 1994 for a review). Hence, a number of demand equations are often constructed for estimation, with the depen