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Time Varying Dimension Models

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  • Joshua C C Chan

    ()

  • Gary Koop

    ()

  • Roberto Leon-Gonzales

    ()

  • Rodney W Strachan

    ()

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 many standard benchmarks and shrink towards parsimonious specifications.

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Bibliographic Info

Paper provided by Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University in its series CAMA Working Papers with number 2011-28.

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Length: 27 pages
Date of creation: Aug 2011
Date of revision:
Handle: RePEc:een:camaaa:2011-28

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References

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Blog mentions

As found by EconAcademics.org, the blog aggregator for Economics research:
  1. Do not waste degrees of freedom with macro data
    by Economic Logician in Economic Logic on 2011-06-30 14:21:00
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Cited by:
  1. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  2. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," CORE Discussion Papers 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  3. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
  4. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics 2013-604, Australian National University, College of Business and Economics, School of Economics.
  5. Miguel, Belmonte & Gary, Koop, 2013. "Model Switching and Model Averaging in Time- Varying Parameter Regression Models," SIRE Discussion Papers 2013-34, Scottish Institute for Research in Economics (SIRE).
  6. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
  7. Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics 2012-591, Australian National University, College of Business and Economics, School of Economics.
  8. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," CAMA Working Papers 2014-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  9. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.
  10. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.

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