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K-state switching models with endogenous transition distributions

  • Sylvia Kaufmann

Two Bayesian sampling schemes are outlined to estimate a K-state Markov switching model with time-varying transition probabilities. The multinomial logit model for the transition probabilities is alternatively expressed as a random utility model and as a difference random utility model. The estimation uses data augmentation and both sampling schemes can be based on Gibbs sampling. Based on the model estimate, we are able to discriminate the model against a smooth transition model, in which the state probability may be influenced by a variable, but without depending on the past prevailing state. Formulating a definition allows to determine the relevant threshold level of the covariate influencing the transition distribution without resorting to the usual grid search. Identification issues are addressed with random permutation sampling. In terms of efficiency the extension to difference random utility in combination with random permutation sampling performs best. To illustrate the method, we estimate a two-pillar Phillips curve for the euro area, in which the inflation rate depends on the low-frequency components of M3 growth, real GDP growth and the change in the government bond yield, and on the highfrequency component of the output gap. Using recent data series, the effect of the low-frequency component of M3 growth depends on regimes determined by lagged credit growth.

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Paper provided by Swiss National Bank in its series Working Papers with number 2011-13.

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Length: 48 pages
Date of creation: 2011
Date of revision:
Handle: RePEc:snb:snbwpa:2011-13
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  1. Paap, R. & van Dijk, H.K., 2002. "Bayes estimates of Markov trends in possibly cointegrated series: an application to US consumption and income," Econometric Institute Research Papers EI 2002-42, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  2. Sylvia Kaufmann, 2010. "Dating and forecasting turning points by Bayesian clustering with dynamic structure: a suggestion with an application to Austrian data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 309-344.
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  8. Sims, Christopher A. & Waggoner, Daniel F. & Zha, Tao, 2008. "Methods for inference in large multiple-equation Markov-switching models," Journal of Econometrics, Elsevier, vol. 146(2), pages 255-274, October.
  9. Stefan Gerlach & Katrin Assenmacher-Wesche, 2006. "Interpreting Euro area inflation at high and low frequencies," BIS Working Papers 195, Bank for International Settlements.
  10. Amisano, Gianni & Fagan, Gabriel, 2010. "Money growth and inflation: a regime switching approach," Working Paper Series 1207, European Central Bank.
  11. Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
  12. James D. Hamilton & Daniel F. Waggoner & Tao Zha, 2007. "Normalization in Econometrics," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 221-252.
  13. Fruhwirth-Schnatter, Sylvia & Fruhwirth, Rudolf, 2007. "Auxiliary mixture sampling with applications to logistic models," Computational Statistics & Data Analysis, Elsevier, vol. 51(7), pages 3509-3528, April.
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  16. repec:bla:restud:v:73:y:2006:i:4:p:1057-1084 is not listed on IDEAS
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