Neural networks as econometric tool

The flexibility of neural networks to handle complex data patterns of economic variables is well known. In this survey we present a brief introduction to a neural network and focus on two aspects of its flexibility . First, a neural network is used to recover the dynamic properties of a nonlinear system, in particular, its stability by making use of the Lyapunov exponent. Second, a two-stage network is introduced where the usual nonlinear model is combined with time transitions, which may be handled by neural networks. The connection with time-varying smooth transition models is indicated. The procedures are illustrated using three examples: a structurally unstable chaotic model, nonlinear trends in real exchange rates and a time-varying Phillips curve using US data from 1960-1997.

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