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• W3125297974 abstract "We propose a new filtering and smoothing technique for non-linear state-space models. Observed variables are quadratic functions of latent factors following a Gaussian VAR. Stacking the vector of factors with its vectorized outer-product, we form an augmented state vector whose first two conditional moments are known in closed-form. We also provide analytical formulae for the unconditional moments of this augmented vector. Our new Quadratic Kalman Filter (Qkf) exploits these properties to formulate fast and simple filtering and smoothing algorithms. A simulation study first emphasizes that the Qkf outperforms the extended and unscented approaches in the filtering exercise showing up to 70% RMSEs improvement of filtered values. Second, it provides evidence that Qkf-based maximum-likelihood estimates of model parameters always possess lower bias or lower RMSEs than the alternative estimators." @default.
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• W3125297974 date "2015-07-01" @default.
• W3125297974 modified "2023-09-25" @default.
• W3125297974 title "A Quadratic Kalman Filter" @default.
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• W3125297974 doi "https://doi.org/10.1016/j.jeconom.2015.01.003" @default.
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