期刊
JOURNAL OF BUSINESS & ECONOMIC STATISTICS
卷 22, 期 3, 页码 346-357出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/073500104000000190
关键词
importance sampling; Kalman filter; Monte Carlo simulation; simulation smoothing; Stochastic volatility; unobserved components time series
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.
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