Nowcasting Tourism Industry Performance Using High Frequency Covariates

Carl Bonham, Peter Fuleky, James Jones () and Ashley Hirashima ()
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James Jones: UH-Manoa Department of Economics and University of Hawaii Economic Research Organization
Ashley Hirashima: UH-Manoa Department of Economics and University of Hawaii Economic Research Organization

No 2015-13R, Working Papers from University of Hawaii Economic Research Organization, University of Hawaii at Manoa

Abstract: 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. 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.

Keywords: Nowcast; Ragged edge; Mixed frequency models (search for similar items in EconPapers)
JEL-codes: H51 I12 Q51 Q53 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2015-09, Revised 2016-07
New Economics Papers: this item is included in nep-for
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https://uhero.hawaii.edu/wp-content/uploads/2020/03/WP_2015-13R.pdf First version, 2015 (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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Persistent link: https://EconPapers.repec.org/RePEc:hae:wpaper:2015-13r

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