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

By Paolo Giordani, Robert Kohn. 2008

235 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Demography, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/522a3569f835833f8cc2b912cd7f7f04687cb6cf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Christophe Andrieu
2. John A. Thoms
3. S. Ko
4. Gary Koop
5. Pengyu Liu
6. I. Macneill
7. Stergios B. Fotopoulos
8. Rodney W. Strachan

In Demography

1. Rodney W. Strachan
2. S. Ko
3. Markus Jochmann
4. Pulak Ghosh
5. Terence Tai-Leung Chong
6. John A. Thoms
7. Gary Koop
8. Pengyu Liu

In Statistics and Probability

1. Gary Koop
2. S. Ko
3. Pulak Ghosh
4. Christophe Andrieu
5. John A. Thoms
6. Pengyu Liu
7. I. Macneill
8. Stergios B. Fotopoulos

Citations in the corpus

• Real-Time Inflation Forecasting in a Changing World (2009, unknown): “As the point forecast we can use the posterior mean or median of the predictive distribution, depending on the chosen loss function (see Gneiting 2011).”
• Bayesian Multivariate Time Series Methods for Empirical Macroeconomics (2010, unknown): “This has recently been used in models of relevance for empirical macroeconomics in Giordani, Kohn and van Dijk (2007) and Giordani and Kohn (2008) and applied to TVP-VARs in Koop, Leon-Gonzalez and Strachan (2009).”
• Bayesian forecasting using stochastic search variable selection in a VAR subject to breaks (2010, background): “Doan, T., Litterman, R. and Sims, C. (1984).”
• Inference for single and multiple change‐points in time series (2013, methodology): “…include those of Yao (1988), Barry and Hartigan (1992, 1993), Chib (1998), Wang and Zivot (2000), Caussinus and Mestre (2004), Fearnhead (2006), Zhao and Chu (2006), Seidou and Ouarda (2007), Giordani and Kohn (2008), Koop and Potter (2009), Chong and Ko (2012) and Lai and Xing (2011, 2012).”
• Time Varying Dimension Models (2012, unknown): “Australian National University ( joshua.chan@anu.edu.au)”
• A tutorial on adaptive MCMC (2008, methodology): “This later choice is closely related to the “fast K-mean” algorithm used in Giordani and Kohn (2006) (although the algorithm developed there is not on-line, whereas the algorithm developed here is computationally very efficient) which is beneficial in the initial stages of the algorithm in order to ”
• Time-Varying Effect of Oil Market Shocks on the Stock Market (2015, unknown): “…the error covariance matrix can change over time.1 The change in parameters overtime is modeled based on the mixture innovation method of Gerlach et al. (2000) and Giordani and Kohn (2008), that is regarded as a special Bayesian stochastic search approach to selecting restrictions for VAR models.”
• 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).”