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• W3125961894 abstract "An exact maximum likelihood method is developed for the estimation of parameters in a non-Gaussian nonlinear log-density function that depends on a latent Gaussian dynamic process with long-memory properties. Our method relies on the method of importance sampling and on a linear Gaussian approximating model from which the latent process can be simulated. Given the presence of a latent long-memory process, we require a modification of the importance sampling technique. In particular, the long-memory process needs to be approximated by a finite dynamic linear process. Two possible approximations are discussed and are compared with each other. We show that an auto-regression obtained from minimizing mean squared prediction errors leads to an effective and feasible method. In our empirical study we analyze ten log-return series from the S&P 500 stock index by uni variate and multivariate long-memory stochastic volatility models." @default.
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• W3125961894 date "2011-01-01" @default.
• W3125961894 modified "2023-09-25" @default.
• W3125961894 title "Monte Carlo Maximum Likelihood Estimation for Generalized Long-Memory Time Series Models" @default.
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• W3125961894 doi "https://doi.org/10.2139/ssrn.1873225" @default.
• W3125961894 hasPublicationYear "2011" @default.
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