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Time Varying Dimension Models

  • Joshua Chan

    ()

    (Australian National University)

  • Gary Koop

    ()

    (Department of Economics, University of Strathclyde)

  • Roberto Leon-Gonzalez

    ()

    (National Graduate Institute for Policy Studies)

  • Rodney Strachan

    ()

    (The Australian National University)

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.

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Paper provided by University of Strathclyde Business School, Department of Economics in its series Working Papers with number 1116.

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Length: 34 pages
Date of creation: Apr 2011
Date of revision:
Handle: RePEc:str:wpaper:1116
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