Time varying dimension models

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

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
JournalJournal of Business and Economic Statistics
Publication statusAccepted/In press - 2012

Keywords

  • Bayesian models
  • time varying parameter model

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