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