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Nowcasting Tourism Industry Performance Using High Frequency Covariates

Listed author(s):
  • Carl Bonham

    ()

    (UH-Manoa Department of Economics, University of Hawaii Economic Research Organization)

  • Peter Fuleky

    ()

    (UH-Manoa Department of Economics and University of Hawaii Economic Research Organization)

  • 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)

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.

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File URL: http://www.uhero.hawaii.edu/assets/WP_2015-13R.pdf
File Function: First version, 2015
Download Restriction: no

Paper provided by University of Hawaii Economic Research Organization, University of Hawaii at Manoa in its series Working Papers with number 2015-13R.

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Length: 48 pages
Date of creation: Sep 2015
Date of revision: Jul 2016
Handle: RePEc:hae:wpaper:2015-13r
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