IDEAS home Printed from
MyIDEAS: Login to save this paper or follow this series

Model Switching and Model Averaging in Time-Varying Parameter Regression Models

  • Miguel Belmonte


    (Department of Economics, University of Strathclyde)

  • Gary Koop


    (Department of Economics, University of Strathclyde)

This paper investigates the usefulness of switching Gaussian state space models as a tool for implementing dynamic model selecting (DMS) or averaging (DMA)in time-varying parameter regression models. DMS methods allow for model switching, where a different model can be chosen at each point in time. Thus, they allow for the explanatory variables in the time-varying parameter regression model to change over time. DMA will carry out model averaging in a time-varying manner. We compare our exact approach to DMA/DMS to a popular existing procedure which relies on the use of forgetting factor approximations. In an application, we use DMS to select different predictors in an in‡ation forecasting application. We also compare different ways of implementing DMA/DMS and investigate whether they lead to similar results.

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:
Download Restriction: no

Paper provided by University of Strathclyde Business School, Department of Economics in its series Working Papers with number 1302.

in new window

Length: 26 pages
Date of creation: Jan 2013
Date of revision:
Publication status: Published
Handle: RePEc:str:wpaper:1302
Contact details of provider: Postal: Sir William Duncan Building, 130 Rottenrow, Glasgow G4 0GE
Phone: +44 (0)141 548 3842
Fax: +44 (0)141 548 4445
Web page:

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. D'Agostino, Antonello & Gambetti, Luca & Giannone, Domenico, 2009. "Macroeconomic Forecasting and Structural Change," CEPR Discussion Papers 7542, C.E.P.R. Discussion Papers.
  2. Gary Koop & Lise Tole, 2013. "Forecasting the European carbon market," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(3), pages 723-741, 06.
  3. Tyler H. McCormick & Adrian E. Raftery & David Madigan & Randall S. Burd, 2012. "Dynamic Logistic Regression and Dynamic Model Averaging for Binary Classification," Biometrics, The International Biometric Society, vol. 68(1), pages 23-30, 03.
  4. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
  5. Koop, Gary & Korobilis, Dimitris, 2012. "Large time-varying parameter VARs," MPRA Paper 38591, University Library of Munich, Germany.
  6. Massimo Guidolin & Allan Timmerman, 2007. "Forecasts of U.S. short-term interest rates: a flexible forecast combination approach," Working Papers 2005-059, Federal Reserve Bank of St. Louis.
  7. Sylvia Frühwirth-Schnatter, 2001. "Fully Bayesian Analysis of Switching Gaussian State Space Models," Annals of the Institute of Statistical Mathematics, Springer, vol. 53(1), pages 31-49, March.
  8. 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.
  9. Joshua C.C. Chan & Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2012. "Time Varying Dimension Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 358-367, January.
  10. Cogley, Timothy & Morozov, Sergei & Sargent, Thomas J., 2005. "Bayesian fan charts for U.K. inflation: Forecasting and sources of uncertainty in an evolving monetary system," Journal of Economic Dynamics and Control, Elsevier, vol. 29(11), pages 1893-1925, November.
  11. 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.
  12. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, 04.
  13. 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.
  14. 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.
  15. Chang-Jin Kim & Charles R. Nelson, 1999. "State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262112388, June.
Full references (including those not matched with items on IDEAS)

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

When requesting a correction, please mention this item's handle: RePEc:str:wpaper:1302. 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: (Kirsty Hall)

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.