  In this paper, the estimation of parameters in the harmonic regression with
cyclically dependent errors is addressed. Asymptotic properties of the
least-squares estimates are analyzed by simulation experiments. By numerical
simulation, we prove that consistency and asymptotic normality of the
least-squares parameter estimator studied holds under different scenarios,
where theoretical results do not exist, and have yet to be proven. In
particular, these two asymptotic properties are shown by simulations for the
least-squares parameter estimator in the non-linear regression model analyzed,
when its error term is defined as a non-linear transformation of a Gaussian
random process displaying long-range dependence.
