Estimating structural exchange rate models by artificial neural networks
No theory of structural exchange rate determination has yet been found that performs well in prediction experiments. Only very seldom has the simple random walk model been significantly outperformed. Referring to three, sometimes highly nonlinear, monetary and nonmonetary structural exchange rate models, a feedforward artificial neural network specification is investigated to determine whether it improves the prediction performance of structural and random walk exchange rate models. A new test for univariate nonlinear cointegration is also derived. Important nonlinearities are not detected for monthly data of US dollar rates in Deutsche marks, Dutch guilders, British pounds and Japanese yens.
Volume (Year): 8 (1998)
Issue (Month): 5 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/RAFE20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/RAFE20|
When requesting a correction, please mention this item's handle: RePEc:taf:apfiec:v:8:y:1998:i:5:p:541-551. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Michael McNulty)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.