  Inference and hypothesis testing is constructed on the basis that a specific
model holds for the data. To determine the veracity of conclusions drawn from
such data analyses, one must be able to identify the presence of the assumed
structure within the data. A model verification test is developed for the
presence of a random walk-like structure for variations in the frequency of
complex-valued sinusoidal signals measured in additive Gaussian noise. This
test evaluates the joint inference of the random walk hypothesis tests found in
economics literature that seek random walk behaviours in time series data, with
an additional test to account for how the random walk behaves in frequency
space.
