Moving average stochastic volatility models with application to inflation forecast

DOI10.1016/j.jeconom.2013.05.003zbMath1284.91423OpenAlexW2269698385MaRDI QIDQ2442456

Joshua C. C. Chan

Publication date: 3 April 2014

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://cama.crawford.anu.edu.au/publication/2060/moving-average-stochastic-volatility-models-application-inflation-forecast

zbMATH Keywords

precisionstate spacesparsedensity forecastunobserved components model

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Stochastic models in economics (91B70) Financial applications of other theories (91G80)

Related Items (15)

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Uses Software

• bvarsv

Cites Work

• Unnamed Item
• Bayesian Multivariate Time Series Methods for Empirical Macroeconomics
• Analysis of high dimensional multivariate stochastic volatility models
• Direct fitting of dynamic models using integrated nested Laplace approximations -- INLA
• Stochastic volatility with leverage: fast and efficient likelihood inference
• Simulation smoothing for state-space models: a computational efficiency analysis
• Bayesian semiparametric stochastic volatility modeling
• On Kalman filtering, posterior mode estimation and Fisher scoring in dynamic exponential family regression
• Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
• Bayes inference in regression models with ARMA\((p,q)\) errors
• Generalized autoregressive conditional heteroscedasticity
• Markov chain Monte Carlo methods for stochastic volatility models.
• Stochastic volatility model with leverage and asymmetrically heavy-tailed error using GH skew Student's \(t\)-distribution
• Bayesian analysis of ARMA-GARCH models: a Markov chain sampling approach
• Efficient simulation and integrated likelihood estimation in state space models
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• Handbook of Monte Carlo Methods
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• MODELING STOCHASTIC VOLATILITY: A REVIEW AND COMPARATIVE STUDY
• FORECASTING INFLATION USING DYNAMIC MODEL AVERAGING*
• Computing Bayes Factors Using a Generalization of the Savage-Dickey Density Ratio
• Time Varying Structural Vector Autoregressions and Monetary Policy
• Estimation and Forecasting in Models with Multiple Breaks

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