A Bayesian Time Series Model of Multiple Structural Changes in Level, Trend, and Variance

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A SIMPLE BAYESIAN APPROACH TO MULTIPLE CHANGE-POINTS (2011, methodology): “…approach make use of reversible jump Markov chain Monte Carlo (MCMC) introduced by Green (1995), or Gibbs sampling used in conjunction with Metropolis-Hastings steps, as in Albert and Chib (1993), Chib (1998), Liu and Lawrence (1999), Wang and Zivot (2000), Chib, Nardari and Shephard (2002).”
1 Trend Filtering (2009, background): “(This model is a special type of multiple structural change time series model [100].”
Dirichlet Process Hidden Markov Multiple Change-point Model (2015, methodology): “More recent works on the Bayesian changepoint model include Wang and Zivot (2000), Giordani and Kohn (2008), Pesaran et al. (2006), Maheu and Gordon (2008) and Geweke and Yu (2011).”
Inference for single and multiple change‐points in time series (2013, methodology): “Subsequent developments of the Bayesian approach make use of reversible jump MCMC introduced by Green (1995), or Gibbs sampling used in conjunction with Metropolis–Hastings steps as in Chib (1998), and Wang and Zivot (2000).”
Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns (2006, methodology): “13 The Fed q data are described in Smithers and Wright (2000) and Robertson and Wright (2002).”