Panel data models in tourism research: Innovative applications and methods

Panel data consists of repeated observations on the same cross-section over time (Wooldridge, 2002). In tourism research, the subjects of the cross-sections have usually been countries or regions due to data availability. However, they can also be tourists, households or firms. Nowadays, they can be reached not only through the administration of multi-wave surveys (Seaton and Palmer, 1997), but more often through the access to big data. The possibility to trace the same tourists’ behaviour over time is advantageous and it certainly enriches the analysis with respect to cross-sectional or time series analysis. However, this kind of datasets are usually unbalanced, which impedes the standard application of panel data methods. In some instances, a panel data descriptive analysis may be the only way to obtain new insights (Bronner and De Hoog, 2008). Moreover, its econometric modelling also provides the ability to study dynamic relationships and to take into account the heterogeneity among subjects. Econometric modelling permits the identification of determinants, the quantification of their relevance and the simulation of their effects with further post-estimation analyses. The econometrics of panel data kept evolving during the last 30 years. However, the application of panel data methods to tourism research was still scarce by the beginning of the 2000s. The first papers criticised the limitations of pooled regressions, because they assume that heterogeneity does not exist, this being a very difficult assumption to meet (see for example, Garı́n-Muñoz and Pérez Amaral, 2000). One way to solve this problem is first differencing, which removes the latent heterogeneity from the model (see for example, Ledesma-Rodrı́guez et al., 2001). Similarly, fixed effects (FE) models are based on the idea that differences across groups can be captured in differences in the constant term. In both cases, any time-invariant variable is removed. However, random effects (RE) models can consider time-invariant regressors. Such an approach can be useful to estimate the determinants of international tourism demand for different sets of origins and destinations (Naudé and Saayman, 2005). In an RE model, we can work on an error