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Time Varying Dimension Models

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  • Joshua C.C. Chan

    (Australian National University)

  • Gary Koop

    ()
    (University of Strathclyde; The Rimini Centre for Economic Analysis (RCEA))

  • Roberto Leon-Gonzalez

    (National Graduate Institute for Policy Studies; The Rimini Centre for Economic Analysis (RCEA))

  • Rodney W. Strachan

    (Australian National University; The Rimini Centre for Economic Analysis (RCEA))

Abstract

Time varying parameter (TVP) models have enjoyed an increasing popularity in empirical macroeconomics. However, TVP models are parameter-rich and risk over-fi?tting 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 speci?cations.

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Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 44_10.

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Date of creation: Jan 2010
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Handle: RePEc:rim:rimwps:44_10

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  1. Fabio Canova & Matteo Ciccarelli, 1999. "Forecasting and turning point predictions in a Bayesian panel VAR model," Economics Working Papers 443, Department of Economics and Business, Universitat Pompeu Fabra.
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  7. Gary Koop & Dimitris Korobilis, 2012. "Forecasting Inflation Using Dynamic Model Averaging," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(3), pages 867-886, 08.
  8. Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2008. "Dynamic probabilities of restrictions in state space models: An application to the Phillips curve," Working Paper Series 26-08, The Rimini Centre for Economic Analysis, revised Jan 2008.
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  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. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," CORE Discussion Papers 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
  3. 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.
  4. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
  5. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
  6. Chan, Joshua C.C., 2013. "Moving average stochastic volatility models with application to inflation forecast," Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
  7. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers 3214, Center for Quantitative Economics (CQE), University of Muenster.
  8. 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).
  9. 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.
  10. 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.
  11. 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.

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