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Nowcasting Tourism Industry Performance Using High Frequency Covariates

Author

Listed:
  • Ashley Hirashima

    (Department of Economics, University of Hawaii)

  • James Jones

    (Department of Economics, University of Hawaii)

  • Carl S. Bonham

    (Department of Economics, University of Hawaii)

  • Peter Fuleky

    (Department of Economics, University of Hawaii)

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.

Suggested Citation

  • Ashley Hirashima & James Jones & Carl S. Bonham & Peter Fuleky, 2016. "Nowcasting Tourism Industry Performance Using High Frequency Covariates," Working Papers 201611, University of Hawaii at Manoa, Department of Economics.
    • Handle: RePEc:hai:wpaper:201611
    as

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    File URL: http://www.economics.hawaii.edu/research/workingpapers/WP_16-11.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Eric W. K. See-To & Eric W. T. Ngai, 2018. "Customer reviews for demand distribution and sales nowcasting: a big data approach," Annals of Operations Research, Springer, vol. 270(1), pages 415-431, November.

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    More about this item

    Keywords

    Nowcast; Ragged edge; Mixed frequency models;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • L83 - Industrial Organization - - Industry Studies: Services - - - Sports; Gambling; Restaurants; Recreation; Tourism
    • Z32 - Other Special Topics - - Tourism Economics - - - Tourism and Development

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