A Dynamic Linear Model to Forecast Hotel Registrations in Puerto Rico Using Google Trends Data

TL;DR

This paper introduces a Dynamic Linear Model that leverages Google Trends search query volume data, which varies weekly, to forecast hotel nonresident registrations in Puerto Rico, improving inference and prediction intervals over single-sample approaches.

Contribution

The paper presents a novel Dynamic Linear Model that accounts for the variability in Google Trends data, enhancing forecasting accuracy and inference for hotel registrations.

Findings

Model provides better inference than single-sample data.

Results show improved prediction intervals.

Performance gains are notable for forecasts over 6 months.

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 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.