Nowcasting Tourism Industry Performance Using High Frequency Covariates

Ashley Hirashima, James Jones, Carl Bonham and Peter Fuleky
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Ashley Hirashima: Department of Economics, University of Hawaii
James Jones: Department of Economics, University of Hawaii

No 201611, Working Papers from University of Hawaii at Manoa, Department of Economics

Abstract: We evaluate the short term forecasting performance of methods that systematically incorporate high frequency information via co-variates. Our study provides a thorough introduction of these methods. 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 insignificant gains in predictive accuracy. Anticipating growing popularity of these techniques among empirical analysts, we present practical implementation guidelines to facilitate their adoption.

Keywords: Nowcast; Ragged edge; Mixed frequency models (search for similar items in EconPapers)
JEL-codes: C22 C82 L83 Z32 (search for similar items in EconPapers)
Date: 2016-08
New Economics Papers: this item is included in nep-tur
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http://www.economics.hawaii.edu/research/workingpapers/WP_16-11.pdf First version, 2016 08 (application/pdf)

Related works:
Working Paper: Nowcasting Tourism Industry Performance Using High Frequency Covariates (2016)
Working Paper: Nowcasting Tourism Industry Performance Using High Frequency Covariates (2015)
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