Working Papers 2002 – Abstracts
Choosing Lag Lengths in Nonlinear Dynamic Models
Heather M. Anderson
Given that it is quite impractical to use standard model selection criteria
in a nonlinear modeling context, the builders of nonlinear models often
choose lag length by setting it equal to the lag length chosen for a linear
autoregression of the data. This paper studies the performance of this
procedure in a variety of circumstances, and then proposes some new and
simple model selection procedures, based on linear approximations of the
nonlinear forms. The idea here is to apply standard selection criteria
to these linear approximations, rather than to autoregressions that make
no provision for nonlinear behavior. A simulation study compares the properties
of these proposed procedures with the properties of linear selection procedures.
Keywords: Nonlinear time series models, Neural networks, Model selection
criteria, Polynomial approximations, Volterra expansions.
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