Estimating time series and cross section tourism demand models: Mainland United States to Hawaii data.

Abstract: A study of factors affecting the number of visitors to Hawaii during the period 1993–2007 prompted by an observed waning of the U. S. mainland to Hawaii visitor market was conducted. Both time series and cross section analyses revealed that Log Gross State Product, Log Chained Airfare and Log Distance to Orlando, Florida were the most important predictor variables. A mixed model which modeled Log Chained Gross State Product, Log Chained Airfare, two recessions plus the September 11, 2001 effect in addition to other fixed effects and random state effects was used. Cross section (spatial) airfare elasticities on an annual basis were high and growing over time, but those estimated from the time series analysis (temporal) were much lower. Elasticities derived from Gross State Product were moderately high and very stable over time. To counteract the distance effect, stopovers in existing mainland resort cities when en route to Hawaii and other promotions to develop a stronger presence of a Hawaii image were recommended. [ABSTRACT FROM AUTHOR]