Forecasting in a Mixed Up World: Nowcasting Hawaii Tourism

• Published in: Annals of tourism research 2017-03, Vol.63, p.191-202
• Main Authors: Hirashima, Ashley, Jones, James, Bonham, Carl S., Fuleky, Peter
• Format: Article
• Language: English
• Subjects: Aggregates; Food service; Forecasting; Gains; Mixed frequency models; Nowcast; Popularity; Ragged edge; Studies; Tourism
• ISSN: 0160-7383; 1873-7722
• DOI: 10.1016/j.annals.2017.01.007

•Evaluate forecasting methods incorporating high frequency information via co-variates.•Our study is a thorough introduction of these methods to the tourism literature.•Highlight the distinguishing features and limitations of each tool.•Predict quarterly tourist arrivals, labor income in accommodations and food sector.•Results: timely intra-period info leads to signicant gains in predictive accuracy. We evaluate the short term forecasting performance of methods that systematically incorporate high frequency information via covariates. Our study provides a thorough introduction of these methods to the tourism literature. We highlight the distinguishing features and limitations of each tool and evaluate their forecasting performance in two tourism-specific applications. The first uses monthly indicators to predict quarterly tourist arrivals to Hawaii; the second predicts quarterly labor income in the accommodations and food services sector. Our results indicate that compared to the exclusive use of low frequency aggregates, including timely intra-period data in the forecasting process results in significant gains in predictive accuracy. Anticipating growing popularity of these techniques among empirical analysts, we present practical implementation guidelines to facilitate their adoption.