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We propose a new approach for constructing synthetic pseudo-panel data fromcross-sectional data. The pseudo panel and the preferences it intends todescribe is constructed at the individual level and is not affected byaggregation bias across cohorts. This is accomplished by creating ahigh-dimensional probabilistic model representation of the entire data set,which allows sampling from the probabilistic model in such a way that all ofthe intrinsic correlation properties of the original data are preserved. Thekey to this is the use of deep learning algorithms based on the ConditionalVariational Autoencoder (CVAE) framework. From a modelling perspective, theconcept of a model-based resampling creates a number of opportunities in thatdata can be organized and constructed to serve very specific needs of which theforming of heterogeneous pseudo panels represents one. The advantage, in thatrespect, is the ability to trade a serious aggregation bias (when aggregatinginto cohorts) for an unsystematic noise disturbance. Moreover, the approachmakes it possible to explore high-dimensional sparse preference distributionsand their linkage to individual specific characteristics, which is not possibleif applying traditional pseudo-panel methods. We use the presented approach toreveal the dynamics of transport preferences for a fixed pseudo panel ofindividuals based on a large Danish cross-sectional data set covering theperiod from 2006 to 2016. The model is also utilized to classify individualsinto 'slow' and 'fast' movers with respect to the speed at which theirpreferences change over time. It is found that the prototypical fast mover is ayoung woman who lives as a single in a large city whereas the typical slowmover is a middle-aged man with high income from a nuclear family who lives ina detached house outside a city.

Introducing Super Pseudo Panels: Application to Transport Preference Dynamics