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.
|Neural networks, Nonlinearity, Phillips curve, Time varying smooth transitions|
|Econometric Institute Research Papers|
|Organisation||Erasmus School of Economics|
Kaashoek, J.F, & van Dijk, H.K. (2000). Neural networks as econometric tool (No. EI 2000-31/A). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/1661