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Time Varying Dimension Models

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

No 1116, Working Papers from University of Strathclyde Business School, Department of Economics

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 paper 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 US in?ation forecasting illustrates and compares the different TVD models. We find our TVD approaches exhibit better forecasting performance than several standard benchmarks and shrink towards parsimonious specifications.

Keywords: mixture model; model change; Bayesian (search for similar items in EconPapers)
JEL-codes: C11 C24 C32 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2011-04
New Economics Papers: this item is included in nep-for
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Related works:
Journal Article: Time Varying Dimension Models (2012) 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
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