A hybrid MIDAS approach for forecasting hotel demand using large panels of search data

8Citations
Citations of this article
23Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

The large amounts of hospitality and tourism-related search data sampled at different frequencies have long presented a challenge for hospitality and tourism demand forecasting. This study aims to evaluate the applicability of large panels of search series sampled at daily frequencies to improve the forecast precision of monthly hotel demand. In particular, a hybrid mixed-data sampling regression approach integrating a dynamic factor model and forecast combinations is the first reported method to incorporate mixed-frequency data while remaining parsimonious and flexible. A case study is undertaken by investigating Sanya, the southernmost city in Hainan province, as a tourist destination using 9 years of the experimental data set. Dynamic factor analysis is used to extract the information from large panels of web search series, and forecast combinations are attempted to obtain the final prediction results of the individual forecasts to enhance the prediction accuracy further. The empirical analysis results suggest that the developed hybrid forecast approach leads to improvements in monthly forecasts of hotel occupancy over its competitors.

Cite

CITATION STYLE

APA

Zhang, B., Li, N., Law, R., & Liu, H. (2022). A hybrid MIDAS approach for forecasting hotel demand using large panels of search data. Tourism Economics, 28(7), 1823–1847. https://doi.org/10.1177/13548166211015515

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free