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Time Varying Dimension Models

  • 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))

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
Date of revision:
Handle: RePEc:rim:rimwps:44_10
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  1. 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.
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  6. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
  7. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-time inflation forecasting in a changing world," Staff Reports 388, Federal Reserve Bank of New York.
  8. Canova, Fabio & Ciccarelli, Matteo, 2004. "Forecasting and turning point predictions in a Bayesian panel VAR model," Journal of Econometrics, Elsevier, vol. 120(2), pages 327-359, June.
  9. Koop, Gary & Korobilis, Dimitris, 2011. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers 2011-40, Scottish Institute for Research in Economics (SIRE).
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  15. 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.
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  18. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney W., 2010. "Dynamic Probabilities of Restrictions in State Space Models: An Application to the Phillips Curve," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(3), pages 370-379.
  19. Matteo Ciccarelli & Alessandro Rebucci, 2001. "The Transmission Mechanism of European Monetary Policy: Is There Heterogeneity? Is It Changing Over Time?," Banco de Espa�a Working Papers 0115, Banco de Espa�a.
  20. 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.
  21. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  22. Ballabriga, Fernando & Sebastian, Miguel & Valles, Javier, 1999. "European asymmetries," Journal of International Economics, Elsevier, vol. 48(2), pages 233-253, August.
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