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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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Bibliographic Info

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. Giordani, Paolo & Kohn, Robert, 2008. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 66-77, January.
  2. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, 03.
  3. Andrew Ang & Geert Bekaert & Min Wei, 2005. "Do Macro Variables, Asset Markets or Surveys Forecast Inflation Better?," NBER Working Papers 11538, National Bureau of Economic Research, Inc.
  4. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996. "Stochastic Volatility: Likelihood Inference And Comparison With Arch Models," Econometrics 9610002, EconWPA.
  5. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-Time Inflation Forecasting in a Changing World," Working Paper 2009/16, Norges Bank.
  6. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES 2009_020, ULB -- Universite Libre de Bruxelles.
  7. 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.
  8. John Geweke & Gianni Amisano, 2007. "Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns," Working Papers 0705, University of Brescia, Department of Economics.
  9. Fabio Canova, 2007. "Bayesian Time Series and DSGE Models, from Methods for Applied Macroeconomic Research
    [Methods for Applied Macroeconomic Research]
    ," Introductory Chapters, Princeton University Press.
  10. Gary Koop & Dimitris Korobilis, 2009. "Forecasting Inflation Using Dynamic Model Averaging," Working Paper Series 34_09, The Rimini Centre for Economic Analysis, revised Jan 2009.
  11. Matteo Ciccarelli & Alessandro Rebucci, 2002. "The Transmission Mechanism of European Monetary Policy: Is There Heterogeneity? Is it Changing over Time?," IMF Working Papers 02/54, International Monetary Fund.
  12. Fabio Canova, 2007. "DSGE Models, Solutions, and Approximations, from Methods for Applied Macroeconomic Research
    [Methods for Applied Macroeconomic Research]
    ," Introductory Chapters, Princeton University Press.
  13. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March.
  14. 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.
  15. Douglas Staiger & James H. Stock & Mark W. Watson, 1997. "The NAIRU, Unemployment and Monetary Policy," Journal of Economic Perspectives, American Economic Association, vol. 11(1), pages 33-49, Winter.
  16. Chib, Siddhartha & Greenberg, Edward, 1995. "Hierarchical analysis of SUR models with extensions to correlated serial errors and time-varying parameter models," Journal of Econometrics, Elsevier, vol. 68(2), pages 339-360, August.
  17. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  18. Ballabriga, Fernando & Sebastian, Miguel & Valles, Javier, 1999. "European asymmetries," Journal of International Economics, Elsevier, vol. 48(2), pages 233-253, August.
  19. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2009. "On the evolution of the monetary policy transmission mechanism," Journal of Economic Dynamics and Control, Elsevier, vol. 33(4), pages 997-1017, April.
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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. 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).
  2. Gary Koop & Dimitris Korobilis, 2012. "Large Time-Varying Parameter VARs," Working Paper Series 11_12, The Rimini Centre for Economic Analysis.
  3. Joshua C.C. Chan, 2013. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," CAMA Working Papers 2013-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  4. 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.
  5. 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.
  6. Dimitris Korobilis, 2010. "VAR Forecasting Using Bayesian Variable Selection," Working Paper Series 51_10, The Rimini Centre for Economic Analysis, revised Apr 2011.
  7. Kalli, Maria & Griffin, Jim E., 2014. "Time-varying sparsity in dynamic regression models," Journal of Econometrics, Elsevier, vol. 178(2), pages 779-793.
  8. 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.
  9. Qian, Hang, 2012. "A Flexible State Space Model and its Applications," MPRA Paper 38455, University Library of Munich, Germany.
  10. 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.

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