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• W2055196903 abstract "This article considers a combination of the linear Gaussian state space model and the stochastic volatility model. The focus is on the simultaneous estimation of parameters related to the stochastic processes of both the mean and variance parts of the model. Kalman filter and Monte Carlo maximum likelihood methods lead to an elegant estimation procedure for which the simulation error can be made arbitrarily small. The standard asymptotic properties of maximum likelihood estimators apply as a result. A Monte Carlo simulation study is carried out to investigate the small-sample properties of the estimation procedure. The modeling and forecasting techniques within our new framework are illustrated for U.S. monthly inflation rates." @default.
• W2055196903 created "2016-06-24" @default.
• W2055196903 creator A5030821922 @default.
• W2055196903 creator A5056085635 @default.
• W2055196903 date "2004-07-01" @default.
• W2055196903 modified "2023-09-24" @default.
• W2055196903 title "State Space Models With a Common Stochastic Variance" @default.
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• W2055196903 doi "https://doi.org/10.1198/073500104000000190" @default.
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