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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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File URL: http://cama.crawford.anu.edu.au/pdf/working-papers/2011/282011.pdf
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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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  1. Dimitris Korobilis, 2010. "VAR Forecasting Using Bayesian Variable Selection," Working Paper Series, The Rimini Centre for Economic Analysis 51_10, The Rimini Centre for Economic Analysis, revised Apr 2011.
  2. John Geweke & Gianni Amisano, 2007. "Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns," Working Papers, University of Brescia, Department of Economics 0705, University of Brescia, Department of Economics.
  3. Koop, Gary & Korobilis, Dimitris, 2010. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2010-113, Scottish Institute for Research in Economics (SIRE).
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Citations

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
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
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Cited by:
  1. Chan, Joshua C.C., 2013. "Moving average stochastic volatility models with application to inflation forecast," Journal of Econometrics, Elsevier, Elsevier, vol. 176(2), pages 162-172.
  2. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
  3. Koop, Gary & Korobilis, Dimitris, 2012. "Large Time-Varying Parameter VARs," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2012-14, Scottish Institute for Research in Economics (SIRE).
  4. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, Elsevier, vol. 177(2), pages 185-198.
  5. KOROBILIS, Dimitris, 2011. "VAR forecasting using Bayesian variable selection," CORE Discussion Papers, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE) 2011022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, Elsevier, vol. 178(2), pages 779-793.
  7. Miguel, Belmonte & Gary, Koop, 2013. "Model Switching and Model Averaging in Time- Varying Parameter Regression Models," SIRE Discussion Papers, Scottish Institute for Research in Economics (SIRE) 2013-34, Scottish Institute for Research in Economics (SIRE).
  8. Eric Eisenstat & Joshua C.C. Chan & Rodney W. Strachan, 2014. "Stochastic Model Specification Search for Time-Varying Parameter VARs," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University 2014-23, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  9. Joshua C.C. Chan & Eric Eisenstat, 2013. "Gibbs Samplers for VARMA and Its Extensions," ANU Working Papers in Economics and Econometrics, Australian National University, College of Business and Economics, School of Economics 2013-604, Australian National University, College of Business and Economics, School of Economics.
  10. Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  11. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Exchange Rates under Model and Parameter Uncertainty," CQE Working Papers, Center for Quantitative Economics (CQE), University of Muenster 3214, Center for Quantitative Economics (CQE), University of Muenster.
  12. Jouchi Nakajima & Mike West, 2013. "Bayesian Analysis of Latent Threshold Dynamic Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, Taylor & Francis Journals, vol. 31(2), pages 151-164, April.

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