Measuring tourism demand nowcasting performance using a monotonicity test

Abstract

Tourism demand nowcasting is generally carried out using econometric models that incorporate either macroeconomic variables or search query data as explanatory variables. Nowcasting model accuracy is normally evaluated by traditional loss functions. This study proposes a novel statistical method, the monotonicity test, to assess whether the nowcasting errors obtained from the ordinary least squares, generalised dynamic factor model and generalised dynamic factor model combined with mixed data sampling model are monotonically decreasing when new data on explanatory variables become available, based on the mixed frequency data between 1 January 2011 and 31 December 2019. The results of the empirical analysis show that nowcasts generated results based on two data sources combined are superior to that based on a single data source. Compared with traditional loss functions, the monotonicity test leads to a more objective and convincing nowcasting model performance. This study is the first attempt to evaluate tourism demand nowcasting performance using a monotonicity test.

Nowcasting tourist nights spent using innovative human mobility data

“…For methods involving explanatory or predictor variables, web search data has become a recurrent choice [30,[34][35][36], even for forecasting at high spatial resolution such as municipalities [22], and sometimes in combination with economic (e.g. prices) data [37]. Aggregated Google Trend data for Hong Kong's tourism demand forecasting suggested that Google Trends' data about a destination may be useful in predicting visits to that destination [38].…”