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Roberto Rivera

University of Puerto Rico at Mayaguez



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599 – Business and Economic Analytics

Assessing the Use of Google Trends Search Query Data to Forecast Number of Nonresident Hotel Registrations in Puerto Rico

Sponsor: Business and Economic Statistics Section
Keywords: combining multiple time series, dynamic linear model, hotel registrations, search query volume data, Google Trends, forecasting models

Roberto Rivera

University of Puerto Rico at Mayaguez

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 SQV than using Google Trends data retrieved only on one occasion. However, compared to simpler models we only find evidence of better performance when making forecasts on a horizon of over 6 months.

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