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). <o:p></o:p></P>
<ul>
  <li>Franses, P. H. &amp; D. J. C. Van Dijk (2000), 
<I>Non-linear Time Series:<SPAN style="mso-spacerun: yes">&nbsp; </SPAN>Models 
in Empirical Finance</I>. Cambridge, U. K.: Cambridge University 
Press.</li>
  <li><SPAN Plous, 
S. (1993), <I>Remus, W. &amp; M. O’Connor (2001), “Neural networks for 
time-series forecasting,” in J. S. Armstrong (ed.), <I>Principles of 
Forecasting</I>. Norwell, MA: Kluwer Academic Press.</SPAN></li>
</ul>