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Time Varying Dimension Models

Joshua Chan, Gary Koop (), Roberto Leon-Gonzales and Rodney Strachan ()
Authors registered in the RePEc Author Service: Roberto Leon-Gonzalez ()

CAMA Working Papers from Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University

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 out TVD approaches exhibit better forecasting performance than many standard benchmarks and shrink towards parsimonious specifications.

Date: 2011-08
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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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