  Functional panels are collections of functional time series, and arise often
in the study of high frequency multivariate data. We develop a portmanteau
style test to determine if the cross-sections of such a panel are independent
and identically distributed. Our framework allows the number of functional
projections and/or the number of time series to grow with the sample size. A
large sample justification is based on a new central limit theorem for random
vectors of increasing dimension. With a proper normalization, the limit is
standard normal, potentially making this result easily applicable in other FDA
context in which projections on a subspace of increasing dimension are used.
The test is shown to have correct size and excellent power using simulated
panels whose random structure mimics the realistic dependence encountered in
real panel data. It is expected to find application in climatology, finance,
ecology, economics, and geophysics. We apply it to Southern Pacific sea surface
temperature data, precipitation patterns in the South-West United States, and
temperature curves in Germany.
