IDEAS home Printed from https://ideas.repec.org/
MyIDEAS: Login to save this paper or follow this series

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.

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://www.rcfea.org/RePEc/pdf/wp44_10.pdf
Download Restriction: no

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

as
in new window

Length:
Date of creation: Jan 2010
Date of revision:
Handle: RePEc:rim:rimwps:44_10
Contact details of provider: Postal:
Via Patara, 3, 47921 Rimini (RN)

Phone: +390541434142
Fax: +39054155431
Web page: http://www.rcfea.org
Email:


More information through EDIRC

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

as in new window
  1. Koop, Gary & Korobilis, Dimitris, 2010. "Forecasting Inflation Using Dynamic Model Averaging," SIRE Discussion Papers 2010-113, Scottish Institute for Research in Economics (SIRE).
  2. Fabio Canova & Matteo Ciccarelli, 2000. "Forecasting And Turning Point Predictions In A Bayesian Panel Var Model," Working Papers. Serie AD 2000-05, Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie).
  3. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, 02.
  4. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2009. "Macroeconomic Forecasting and Structural Change," Working Papers ECARES 2009_020, ULB -- Universite Libre de Bruxelles.
  5. Ang, Andrew & Bekaert, Geert & Wei, Min, 2007. "Do macro variables, asset markets, or surveys forecast inflation better?," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1163-1212, May.
  6. 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.
  7. James H. Stock & Mark W. Watson, 2008. "Phillips curve inflation forecasts," Conference Series ; [Proceedings], Federal Reserve Bank of Boston, vol. 53.
  8. Jan J. J. Groen & Richard Paap & Francesco Ravazzolo, 2009. "Real-Time Inflation Forecasting in a Changing World," Working Paper 2009/16, Norges Bank.
  9. 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.
  10. 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.
  11. 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.
  12. Ballabriga, Fernando & Sebastian, Miguel & Valles, Javier, 1999. "European asymmetries," Journal of International Economics, Elsevier, vol. 48(2), pages 233-253, August.
  13. Giordani, P. & Kohn, R. & van Dijk, D.J.C., 2005. "A unified approach to nonlinearity, structural change and outliers," Econometric Institute Research Papers EI 2005-09, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  14. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, 03.
  15. 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.
  16. Giordani, Paolo & Kohn, Robert, 2006. "Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models," Working Paper Series 196, Sveriges Riksbank (Central Bank of Sweden).
  17. 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.
  18. Fabio Canova, 2007. "DSGE Models, Solutions, and Approximations, from Methods for Applied Macroeconomic Research
    [Methods for Applied Macroeconomic Research]
    ," Introductory Chapters, Princeton University Press.
  19. 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.
  20. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  21. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," Review of Economic Studies, Oxford University Press, vol. 65(3), pages 361-393.
  22. repec:oup:restud:v:72:y:2005:i:3:p:821-852 is not listed on IDEAS
Full references (including those not matched with items on IDEAS)

This item is featured on the following reading lists or Wikipedia pages:

  1. Economic Logic blog

When requesting a correction, please mention this item's handle: RePEc:rim:rimwps:44_10. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Marco Savioli)

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

This information is provided to you by IDEAS at the Research Division of the Federal Reserve Bank of St. Louis using RePEc data.