In this context,
(or, in the time domain, *l*) are no longer
independent variables. They are treated like any of the other
parameters: i.e. are assumed to be random variables to be estimated
from the observations by fitting a model. Parameter estimation in the
frequency domain is best done by fitting models using statistics (least squares). The MIDAS TSA package contains just one
such model, namely Fourier series `(SINEFIT/TSA)`. However, note
that with its non-linear least-squares fitting package, MIDAS offers
very versatile, dedicated tools for model fitting (see Chapter 8 in
Vol. 8 of the MIDAS User Guide).

In the time domain, the most important parameters to be estimated from
the data are the correlation length of and time lag between the input
signals. This measurement can be done with the command `WIDTH/TSA`. The correlation length can be obtained as the width of
the line centered at zero lag. The time lag can be measured as the
center of the corresponding line in the ACF.