Time varying dimension models

Chan, JCC; Koop, G; Leon-Gonzalez, R; Strachan, RW

Time varying dimension models

Chan, JCC

Koop, G Leon-Gonzalez, R Strachan, RW

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Publication Type:: Journal Article
Citation:: Journal of Business and Economic Statistics, 2012, 30 (3), pp. 358 - 367
Issue Date:: 2012-07-30

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Field	Value	Language
dc.contributor.author	Chan, JCC https://orcid.org/0000-0003-3632-128X	en_US
dc.contributor.author	Koop, G	en_US
dc.contributor.author	Leon-Gonzalez, R	en_US
dc.contributor.author	Strachan, RW	en_US
dc.date.issued	2012-07-30	en_US
dc.identifier.citation	Journal of Business and Economic Statistics, 2012, 30 (3), pp. 358 - 367	en_US
dc.identifier.issn	0735-0015	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115039
dc.description.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. © 2012 American Statistical Association.	en_US
dc.relation.ispartof	Journal of Business and Economic Statistics	en_US
dc.relation.isbasedon	10.1080/07350015.2012.663258	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Time varying dimension models	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	30	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	0102 Applied Mathematics	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	14 Economics	en_US
utslib.for	15 Commerce, Management, Tourism and Services	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

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. © 2012 American Statistical Association.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/115039