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

  • Miguel, Belmonte
  • Gary, Koop

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.

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File URL: http://repo.sire.ac.uk/handle/10943/440
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Paper provided by Scottish Institute for Research in Economics (SIRE) in its series SIRE Discussion Papers with number 2013-34.

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Date of creation: 2013
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Handle: RePEc:edn:sirdps:440
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  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. Guidolin, Massimo & Timmermann, Allan, 2009. "Forecasts of US short-term interest rates: A flexible forecast combination approach," Journal of Econometrics, Elsevier, vol. 150(2), pages 297-311, June.
  3. Joshua C C Chan & Gary Koop & Roberto Leon-Gonzales & Rodney W Strachan, 2011. "Time Varying Dimension Models," CAMA Working Papers 2011-28, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
  4. Dangl, Thomas & Halling, Michael, 2012. "Predictive regressions with time-varying coefficients," Journal of Financial Economics, Elsevier, vol. 106(1), pages 157-181.
  5. 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.
  6. D'Agostino, Antonello & Gambetti, Luca & Giannone, Domenico, 2009. "Macroeconomic Forecasting and Structural Change," CEPR Discussion Papers 7542, C.E.P.R. Discussion Papers.
  7. Dimitris Korompilis, 2009. "Assessing the Transmission of Monetary Policy Shocks Using Dynamic Factor Models," Working Papers 0914, University of Strathclyde Business School, Department of Economics.
  8. 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.
  9. 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.
  10. Gary Koop & Dimitris Korobilis, 2012. "Large time-varying parameter VARs," Working Papers 2012_04, Business School - Economics, University of Glasgow.
  11. Gary Koop & Lise Tole, 2011. "Forecasting the European Carbon Market," Working Papers 1110, University of Strathclyde Business School, Department of Economics.
  12. repec:dgr:uvatin:20110172 is not listed on IDEAS
  13. 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.
  14. 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.
  15. 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.
  16. 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.
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