  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. 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.
