High spatial and temporal detail in timely prediction of tourism demand

Abstract

What happens in forecasting problems when high frequency and high spatial detail data encounter significant publication delays? In this paper, we consider a monthly dynamic panel data model, augmented by Google Trends search query volume data, for tourism demand forecasting at high spatial detail, in which one of the main aspects is represented by a publication delay ranging from 8 to 15 months. Some findings in the tourism literature already specify forecasting/nowcasting applications considering a realistic time delay but not for more than 3 months.