Advanced Search
MyIDEAS: Login

Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

Contents:

Author Info

  • Jochmann, Markus

Abstract

This paper uses an infinite hidden Markov model (IIHMM) to analyze U.S. inflation dynamics with a particular focus on the persistence of inflation. The IHMM is a Bayesian nonparametric approach to modeling structural breaks. It allows for an unknown number of breakpoints and is a flexible and attractive alternative to existing methods. We found a clear structural break during the recent financial crisis. Prior to that, inflation persistence was high and fairly constant.

Download Info

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.
File URL: http://repo.sire.ac.uk/handle/10943/139
Download Restriction: no

Bibliographic Info

Paper provided by Scottish Institute for Research in Economics (SIRE) in its series SIRE Discussion Papers with number 2010-06.

as in new window
Length:
Date of creation: 2010
Date of revision:
Handle: RePEc:edn:sirdps:139

Contact details of provider:
Postal: 31 Buccleuch Place, EH8 9JT, Edinburgh
Phone: +44(0)1316508361
Fax: +44(0)1316504514
Email:
Web page: http://www.sire.ac.uk
More information through EDIRC

Related research

Keywords: inflation dynamics; hierarchical Dirichlet process; IHMM; structural breaks; Bayesian nonparametrics;

Other versions of this item:

Find related papers by JEL classification:

References

No references listed on IDEAS
You can help add them by filling out this form.

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Yong Song, 2012. "Modelling Regime Switching and Structural Breaks with an Infinite Hidden Markov Model," Working Paper Series 28_12, The Rimini Centre for Economic Analysis.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:edn:sirdps:139

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Gina Reddie).

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.