Early panel studies of tourism demand have relied on the assumption of cross-sectional independence,
or that each region contributes entirely new information to the dataset. Yet, cross-sectional
units are generally influenced by national or global shocks such as business cycles, technological innovations,
terrorism events, oil crises or national fiscal and monetary policies. We demonstrate that
neglecting cross-sectional dependence leads to spurious estimation results. Our contribution to the literature
lies in estimating tourism demand elasticities while accounting for unobserved non-stationary
common factors in the data. We use the CCE estimators of Pesaran (2006) and Kapetanios et al. (2011)
to deal with cross-sectional dependence in panel regressions. This technique offers several advantages
over competing methods: it does not require ex ante information about the unobserved common factors,
allows them to contain unit roots and to be correlated with the regressors, allows for heterogeneous
factor loadings, exhibits good finite sample properties, and is relatively simple to implement.