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This paper provides a general framework for the quantitative analysis of stochastic dynamic models. We review the convergence properties of some numerical algorithms and available methods to bound approximation errors. We then address the convergence and accuracy properties of the simulated...
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We study the strong consistency and asymptotic normality of the maximum likelihood estimator for a class of time series … processes. We formulate primitive conditions for global identification, invertibility, strong consistency, and asymptotic …
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. We derive considerably weaker conditions that can be used in practice to ensure the consistency of the maximum likelihood … estimator for a wide class of observation-driven time series models. Our consistency results hold for both correctly specified …
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