The semiparametric asymmetric stochastic volatility model with time-varying parameters: the case of US inflation

DOI10.1016/J.ECONLET.2017.02.039zbMath1398.62293OpenAlexW2594743785MaRDI QIDQ1673428

Stefanos Dimitrakopoulos

Publication date: 12 September 2018

Published in: Economics Letters (Search for Journal in Brave)

Full work available at URL: http://eprints.whiterose.ac.uk/141638/1/Manuscript_1.pdf

zbMATH Keywords

Markov chain Monte Carloinflationtime-varying parametersDirichlet processasymmetric stochastic volatility

Mathematics Subject Classification ID

Applications of statistics to actuarial sciences and financial mathematics (62P05) Bayesian inference (62F15) Monte Carlo methods (65C05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Applications of stochastic analysis (to PDEs, etc.) (60H30)

Cites Work

• Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility
• Modeling individual differences using Dirichlet processes
• Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture
• A Bayesian analysis of some nonparametric problems
• The relationship between inflation and inflation uncertainty in the UK: 1885--1998

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