A dynamic linear model to forecast hotel registrations in Puerto Rico using Google Trends data

Roberto Rivera

Tourism Management, 2016, vol. 57, issue C, 12-20

Abstract: Recently, studies have used search query volume (SQV) data to forecast a given process of interest. However, Google Trends SQV data comes from a periodic sample of queries. As a result, Google Trends data is different every week. We propose a Dynamic Linear Model that treats SQV data as a representation of an unobservable process. We apply our model to forecast the number of hotel nonresident registrations in Puerto Rico using SQV data downloaded in 11 different occasions. The model provides better inference on the association between the number of hotel nonresident registrations and Google Trends SQV than using Google Trends data retrieved only on one occasion. Furthermore, our model results in more realistic prediction intervals of forecasts. However, compared to simpler models we only find evidence of better performance for our model when making forecasts on a horizon of over 6 months.

Keywords: Combining multiple time series; Dynamic model; Hotel registrations; Search query volume data; Google Trends; Forecasting models; Prediction intervals (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (47)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:touman:v:57:y:2016:i:c:p:12-20

DOI: 10.1016/j.tourman.2016.04.008

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