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.

Kokoszka, Piotr, Matthew Reimherr and Nikolas Wölfing (2016), A randomness test for functional panels, Journal of Multivariate Analysis 151, 37-53. Download


Kokoszka, Piotr
Reimherr, Matthew
Wölfing, Nikolas


Functional Data Analysis, independence test, panel data, El Nino, climate data