If Nonlinear Models Cannot Forecast, What Use Are They?
James B. Ramsey
Department of Economics
New York University
Abstract
This paper begins with a brief review of the recent experience using
nonlinear models and ideas of chaos to model economic data and to
provide forecasts that are better than linear models. The record of
improvement is at best meager. The remainder of the paper examines
some of the reasons for this lack of improvement. The concepts of
"openness" and "isolation" are introduced, and a case is made that
open and nonisolated systems cannot be forecasted; the extent to which
economic systems are closed and isolated provides the true pragmatic
limits to forecastability. The reasons why local "overfitting,"
especially with nonparametric models, leads to worse forecasts are
discussed. Models and "representations" of data are distinguished and
the reliance on minimum mean-square forecast error to choose between
models and representations is evaluated.
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