EconPapers    
Economics at your fingertips  
 

Time Varying Dimension Models

Joshua Chan, Gary Koop (), Roberto Leon-Gonzalez () and Rodney Strachan ()

Journal of Business & Economic Statistics, 2012, vol. 30, issue 3, 358-367

Abstract: Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fitting unless the dimension of the model is small. Motivated by this worry, this article proposes several Time Varying Dimension (TVD) models where the dimension of the model can change over time, allowing for the model to automatically choose a more parsimonious TVP representation, or to switch between different parsimonious representations. Our TVD models all fall in the category of dynamic mixture models. We discuss the properties of these models and present methods for Bayesian inference. An application involving U.S. inflation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than many standard benchmarks and shrink toward parsimonious specifications. This article has online supplementary materials.

Date: 2012
References: View complete reference list from CitEc
Citations: View citations in EconPapers (44) Track citations by RSS feed

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2012.663258 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Time Varying Dimension Models (2011) Downloads
Working Paper: Time Varying Dimension Models (2011) Downloads
Working Paper: Time Varying Dimension Models (2010) Downloads
Working Paper: Time Varying Dimension Models (2010) Downloads
Working Paper: Time Varying Dimension Models (2010) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:30:y:2012:i:3:p:358-367

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2012.663258

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2021-02-26
Handle: RePEc:taf:jnlbes:v:30:y:2012:i:3:p:358-367