Information paradigms inspired by the way the human brain processes information. They can approximate almost any function on a closed and bounded range and are thus known as universal function approximators. Neural networks are black-box forecasting techniques, and practitioners must rely on ad hoc methods in selecting models. As a result, it is difficult to understand relationships among the variables in the model. Franses and Van Dijk (2000) describe how to compute elasticities from neural nets. See Remus and O’Connor (2001).
- Franses, P. H. & D. J. C. Van Dijk (2000),
*Non-linear Time Series: Models
in Empirical Finance*. Cambridge, U. K.: Cambridge University
Press.
- Remus, W. & M. O’Connor (2001), “Neural networks for
time-series forecasting,” in J. S. Armstrong (ed.),
*Principles of
Forecasting*. Norwell, MA: Kluwer Academic Press.