Search Results - subject:"Hierarchical Dirichlet process"

Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

Jochmann, Markus - Rimini Centre for Economic Analysis (RCEA) - 2010

This paper uses an infinite hidden Markov model (IHMM) to analyze U.S. inflation dynamics with a particular focus on the persistence of inflation. The IHMM is a Bayesian nonparametric approach to modeling structural breaks. It allows for an unknown number of breakpoints and is a flexible and...

Persistent link: https://www.econbiz.de/10008487525

Modeling US inflation dynamics : a Bayesian nonparametric approach

Jochmann, Markus - In: Econometric reviews 34 (2015) 1/5, pp. 537-558

Persistent link: https://www.econbiz.de/10011373257

Modeling Associations Among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

Tchumtchoua, Sylvie; Dey, Dipak - In: Psychometrika 77 (2012) 4, pp. 670-692

are modeled nonparametrically through a dynamic hierarchical Dirichlet process prior. A Markov chain Monte Carlo algorithm …

Persistent link: https://www.econbiz.de/10010848164

Modeling U.S. Inflation Dynamics: A Bayesian Nonparametric Approach

Jochmann, Markus - Scottish Institute for Research in Economics (SIRE) - 2010

This paper uses an infinite hidden Markov model (IIHMM) to analyze U.S. inflation dynamics with a particular focus on the persistence of inflation. The IHMM is a Bayesian nonparametric approach to modeling structural breaks. It allows for an unknown number of breakpoints and is a flexible and...

Persistent link: https://www.econbiz.de/10010553596