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We consider likelihood inference and state estimation by means of importance sampling for state space models with a … and smoother and the simulation smoother which do not rely on a linear Gaussian observation equation. Furthermore, results …
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Likelihood based inference for multi-state latent factor intensity models is hindered by the fact that exact closed-form expressions for the implied data density are not available. This is a common and well-known problem for most parameter driven dynamic econometric models. This paper reviews,...
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In a recent paper Hualde and Robinson (2011) establish consistency and asymptotic normality for conditional sum-of-squares estimators, which are equivalent to conditional quasi-maximum likelihood estimators, in parametric fractional time series models driven by conditionally homoskedastic...
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This paper introduces Quasi-Maximum Likelihood Estimation for Long Memory Stock Transaction Data of unknown underlying … sensitive to start value. Hence, two-stage QML has been suggested. In empirical estimation on two stock transaction data for …
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