  Nonresponse in panel studies can lead to a substantial loss in data quality
due to its potential to introduce bias and distort survey estimates. Recent
work investigates the usage of machine learning to predict nonresponse in
advance, such that predicted nonresponse propensities can be used to inform the
data collection process. However, predicting nonresponse in panel studies
requires accounting for the longitudinal data structure in terms of model
building, tuning, and evaluation. This study proposes a longitudinal framework
for predicting nonresponse with machine learning and multiple panel waves and
illustrates its application. With respect to model building, this approach
utilizes information from multiple waves by introducing features that aggregate
previous (non)response patterns. Concerning model tuning and evaluation,
temporal cross-validation is employed by iterating through pairs of panel waves
such that the training and test sets move in time. Implementing this approach
with data from a German probability-based mixed-mode panel shows that
aggregating information over multiple panel waves can be used to build
prediction models with competitive and robust performance over all test waves.
