  A new approach to nonlinear modelling is presented which, by incorporating
the global behaviour of the model, lifts shortcomings of both least squares and
total least squares parameter estimates. Although ubiquitous in practice, a
least squares approach is fundamentally flawed in that it assumes independent,
normally distributed (IND) forecast errors: nonlinear models will not yield IND
errors even if the noise is IND. A new cost function is obtained via the
maximum likelihood principle; superior results are illustrated both for small
data sets and infinitely long data streams.
