Estimating demand elasticities in non-stationary panels: The case of Hawaii tourism

• We estimate elasticities of tourism demand from the US Mainland to Hawaii.
• We account for non-stationary common factors in panel data.
• We demonstrate that neglecting cross-sectional dependence leads to spurious results.
• Our approach does not require observable proxies for unobserved factors in the data.
• We use an estimator with good finite sample properties that is simple to implement.

It is natural to turn to the richness of panel data to improve the precision of estimated tourism demand elasticities. However, the likely presence of common shocks shared across the underlying macroeconomic variables and across regions in the panel has so far been neglected in the tourism literature. We deal with the effects of cross-sectional dependence by applying Pesaran’s (2006) common correlated effects estimator, which is consistent under a wide range of conditions and is relatively simple to implement. We study the extent to which tourist arrivals from the US Mainland to Hawaii are driven by fundamentals such as real personal income and travel costs, and we demonstrate that ignoring cross-sectional dependence leads to spurious results.