Learning and Inflation Convergence in the ERM
This paper develops a model where agents learn about the probability of devaluations in a fixed exchange rate regim e. The true probability of devaluation is assumed to be low (or zero) b ut agents are initially unsure about the government's intentions and st art with a high prior belief. Bayesian updating dictates that private sector expectations are revised down during periods when no devaluat ion occurs, but are sharply increased when a devaluation does occur. Thi s model of learning is embedded in a model of overlapping contracts to study the process of inflation convergence after ERM entry. The main results are that agents over-estimate the authorities' desired realignment rate, so inflation will be high in anticipation; but as the authorities do not actually devalue, there is a terms of trade loss leading to recession. Before long-run equilibrium in which agents correctly estimate realignment rates can be attained, competitivenes s losses actually have to be regained by a sustained deflation. Copyright 1993 by Royal Economic Society.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 103 (1993)
Issue (Month): 417 (March)
|Contact details of provider:|| Postal: Office of the Secretary-General, Rm E35, The Bute Building, Westburn Lane, St Andrews, KY16 9TS, UK|
Phone: +44 1334 462479
Web page: http://www.res.org.uk/
More information through EDIRC
|Order Information:||Web: http://www.blackwellpublishers.co.uk/asp/journal.asp?ref=0013-0133|
When requesting a correction, please mention this item's handle: RePEc:ecj:econjl:v:103:y:1993:i:417:p:369-78. 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: (Wiley-Blackwell Digital Licensing)or (Christopher F. Baum)
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.