Recently there has been an increased interest in the theory of
chaos by macroeconomists and fmancial economists. Originating in the
natural sciences, applications of the theory have spread through various
fields including brain research, optics, metereology, and economics. The
attractiveness of chaotic dynamics is its ability to generate large
movements which appear to be random, with greater frequency than linear
models. Two of the most striking features of any macro-economic data are
its random-like appearance and its seemingly cyclical character. Cycles
in economic data have often been noticed, from short-run business
cycles, to 50 years Kodratiev waves. There have been many attempts to
explain them, e.g. Lucas (1975), who argues that random shocks combined
with various lags can give rise to phenomena which have the appearance
of cycles, and Samuelson (1939) who uses the familiar multiplier
accelerator model. The advantage of using non-linear difference (or
differential) equation models to explain the business cycle is that it
does not have to rely on ad hoc unexplained exogenous random
shocks.
