Time Varying Dimension Models

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

No 2012-33, SIRE Discussion Papers from Scottish Institute for Research in Economics (SIRE)

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 inflation 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.

Date: 2010-05
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Citations: View citations in EconPapers (3)

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Related works:
Journal Article: Time Varying Dimension Models (2012)
Working Paper: Time Varying Dimension Models (2011)
Working Paper: Time Varying Dimension Models (2011)
Working Paper: Time Varying Dimension Models (2010)
Working Paper: Time Varying Dimension Models (2010)
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