Abstract
Purpose – This study aims to examine whether and when real-time updated online search engine data such as the daily Baidu Index can be useful for improving the accuracy of tourism demand nowcasting once monthly official statistical data, including historical visitor arrival data and macroeconomic variables, become available. Design/methodology/approach – This study is the first attempt to employ the LASSO-MIDAS model proposed by Marsilli (2014) to field of the tourism demand forecasting to deal with the inconsistency in the frequency of data and the curse problem caused by the high dimensionality of search engine data. Findings – The empirical results in the context of visitor arrivals in Hong Kong show that the application of a combination of daily Baidu Index data and monthly official statistical data produces more accurate nowcasting results when MIDAS-type models are employed. The effectiveness of the LASSO-MIDAS model for tourism demand nowcasting indicates that this kind of penalty-based MIDAS model is a useful option when using high-dimensional mixed frequency data. Originality/value – This study represents the first attempt to progressively compare whether there are any differences between using daily search engine data, monthly official statistical data, and a combination of the aforementioned two types of data with different frequencies to nowcast tourism demand. The study also contributes to the tourism forecasting literature by presenting the first attempt to evaluate the applicability and effectiveness of the LASSO-MIDAS model in tourism demand nowcasting.