Semiparametric Bayesian inference for time-varying parameter regression models with stochastic volatility

DOI10.1016/j.econlet.2016.10.035zbMath1400.62079OpenAlexW2550285037MaRDI QIDQ1672741

Stefanos Dimitrakopoulos

Publication date: 11 September 2018

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

Full work available at URL: http://eprints.whiterose.ac.uk/141640/1/manuscript_1.pdf

zbMATH Keywords

stochastic volatilityMarkov chain Monte Carloinflationtime-varying parametersDirichlet processBayesian inference

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Nonparametric regression and quantile regression (62G08) Bayesian inference (62F15)

Related Items (2)

Quasi-maximum exponential likelihood estimator and portmanteau test of double \(\operatorname{AR}(p)\) model based on \(\operatorname{Laplace}(a,b)\) ⋮ The semiparametric asymmetric stochastic volatility model with time-varying parameters: the case of US inflation

Uses Software

• bvarsv

Cites Work

• Bayesian semiparametric stochastic volatility modeling
• Estimating a semiparametric asymmetric stochastic volatility model with a Dirichlet process mixture
• A Bayesian analysis of some nonparametric problems
• Bayesian nonparametric modelling of the return distribution with stochastic volatility
• Efficient simulation and integrated likelihood estimation in state space models
• Noncausality and inflation persistence
• Bayesian Measures of Model Complexity and Fit
• The simulation smoother for time series models
• Bayesian Density Estimation and Inference Using Mixtures
• Noncausal Autoregressions for Economic Time Series
• Time Varying Structural Vector Autoregressions and Monetary Policy

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