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Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models

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Author Info
Giordani, Paolo () (Research Department, Central Bank of Sweden)
Kohn, Robert (School of Economics, School of Banking and Finance)

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Abstract

Time series subject to parameter shifts of random magnitude and timing are commonly modeled with a change-point approach using Chib's (1998) algorithm to draw the break dates. We outline some advantages of an alternative approach in which breaks come through mixture distributions in state innovations, and for which the sampler of Gerlach, Carter and Kohn (2000) allows reliable and efficient inference. We show how this approach can be used to (i) model shifts in variance that occur independently of shifts in other parameters (ii) draw the break dates efficiently in change-point and regime-switching models with either Markov or non-Markov transition probabilities. We extend the proofs given in Carter and Kohn (1994) and in Gerlach, Carter and Kohn (2000) to state-space models with system matrices which are functions of lags of the dependent variables, and we further improve the algorithms in Gerlach, Carter and Kohn by introducing to the time series literature the concept of adaptive Metropolis-Hastings sampling for discrete latent variable models. We develop an easily implemented adative algorithm that promises to sizably reduce computing time in a variety of problems including mixture innovation, change-point, regime-switching, and outlier detection.

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Paper provided by Sveriges Riksbank (Central Bank of Sweden) in its series Working Paper Series with number 196.

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Length: 39 pages
Date of creation: 01 May 2006
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Handle: RePEc:hhs:rbnkwp:0196

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Related research
Keywords: Structural breaks; Parameter instability; Change-point; State-space; Mixtures; Discrete latent variables; Adaptive Metropolis-Hastings;

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Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions

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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.:
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  3. Giordani, Paolo & Kohn, Robert & van Dijk, Dick, 2007. "A unified approach to nonlinearity, structural change, and outliers," Journal of Econometrics, Elsevier, vol. 137(1), pages 112-133, March. [Downloadable!] (restricted)
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  12. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895.
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Full references

Cited by:
(explanations, 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.)

  1. Drew Creal & Siem Jan Koopman & Eric Zivot, 2008. "The effect of the great moderation on the U.S. business cycle in a time-varying multivariate trend-cycle model," Working Papers UWEC-2008-15, University of Washington, Department of Economics. [Downloadable!]
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  2. Dimitris Korobilis, 2009. "Assessing the Transmission of Monetary Policy Shocks Using Dynamic Factor Models," Working Papers 09-14, University of Strathclyde Business School, Department of Economics. [Downloadable!]
  3. John M. Maheu & Stephen Gordon, 2008. "Learning, forecasting and structural breaks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 23(5), pages 553-583. [Downloadable!]
    Other versions:
  4. Gary Koop & Simon Potter, 2007. "A flexible approach to parametric inference in nonlinear time series models," Staff Reports 285, Federal Reserve Bank of New York. [Downloadable!]
  5. John M Maheu & Thomas H McCurdy, 2007. "How useful are historical data for forecasting the long-run equity return distribution?," Working Papers tecipa-293, University of Toronto, Department of Economics. [Downloadable!]
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  6. Gary Koop & Roberto Leon-Gonzalez & Rodney W. Strachan, 2008. "On the Evolution of Monetary Policy," Working Paper Series 24-08, Rimini Centre for Economic Analysis, revised Jan 2008. [Downloadable!]
  7. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2009. "Disagreement among Forecasters in G7 Countries," Macroeconomics and Finance Series 200906, Hamburg University, Department Wirtschaft und Politik. [Downloadable!]
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