Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances

Abstract

This study investigates how age, period, and birth cohorts are related to altering travel distances. We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971–2018 using a holistic age–period–cohort (APC) analysis framework. Changes in travel distances are attributed to the life cycle (age effect), macro-level developments (period effect), and generational membership (cohort effect). We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. Generalized additive models are used to circumvent the identification problem by fitting a bivariate tensor product spline between age and period. The results indicate that participation in short-haul trips is mainly associated with age, while participation in long-distance travel predominantly changed over the period. Generational membership shows less association with destination choice concerning travel distance. The presented APC approach is promising to address further questions of interest in tourism research.

The transition of eldercare responsibility and traditional filial piety concepts and its urban-rural differences in China: an age-period-cohort analysis from 2006 to 2017

“…To ensure the reliability of the HAPC model, we also applied the generalized additive model (GAM) to disentangle the age/period/cohort effect. The GAM addresses the ‘identification problem’ by examining the nonlinear age, period, and cohort effects using a bivariate spline function that combines age and period to indirectly indicate the cohort effect [ 35 ]. Referring to Weigert et al (2022) [ 35 ], we designed the following model:…”

“…The GAM addresses the ‘identification problem’ by examining the nonlinear age, period, and cohort effects using a bivariate spline function that combines age and period to indirectly indicate the cohort effect [ 35 ]. Referring to Weigert et al (2022) [ 35 ], we designed the following model:…”

Panel data models in tourism research: Innovative applications and methods

“…However, the application of non-parametric or semiparametric techniques is not commonly applied in tourism economics and it may enrich the understanding provided by the econometric analysis. Weigert et al (2022) contribute to the tourism literature with a semiparametric technique that comprises the analysis according to the age of tourists, the year (period), and the birth cohort they belong to. It is known as the Age-Period-Cohort (APC) model, which deal with repeated cross-sectional data in a multiplicative way.…”

“…Additionally, Weigert et al (2022) provide heatmaps and ridgeline matrices of results that enrich the understanding of APCs in relation to different key variables such as travel distance. They provide a good illustration of how the employment of APC can enrich the understanding of tourists’ behaviour.…”

Distance Traveled in Times of Pandemic: An Endogenous Switching Regression Approach

“…Wong et al (2016) document that tourists’ outbound travel behavior is not only affected by the business cycle but also by the tourist’s life cycle so that preferences for distant destinations are time-variant. In a recent paper, Weigert et al (2021) exploit repeated cross-sectional survey data for German tourists for the period 1971–2018 and show that the distance traveled mainly depend on the life cycle and macro-level factors, with cohort differences being less pronounced.…”