Researchers studying a variety of important economics, nutrition, and health topics use survey data containing information on SNAP participation. In order to study the dynamics of SNAP participation or recognizing possible selection bias in cross-sectional estimators, many researchers use longitudinal estimators to estimate the causal effects of SNAP. However, misreporting of SNAP participation is common in survey datasets, and bias from misreporting can be larger for longitudinal estimators. In an analysis of data combining newly compiled administrative datasets on SNAP participation from nine states and covering the years 2005-2015 with individual records from the CPS ASEC survey, we confirm findings in previous studies of substantial misreporting and find evidence that the misreporting is not done at random. Additionally, we examine bias caused by misreporting in a longitudinal estimators and find severe bias, much greater in magnitude than bias caused by misreporting in cross-sectional estimators. We find that a longitudinal conditional distribution estimator may be an attractive solution for researchers using public use survey datasets.