A Quadratic Kalman Filter

Détails

ID Serval
serval:BIB_D3636358D4E9
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
A Quadratic Kalman Filter
Périodique
Journal of Econometrics
Auteur(s)
Monfort A., Renne J.-P., Roussellet G.
ISSN
0304-4076
Statut éditorial
Publié
Date de publication
2015
Peer-reviewed
Oui
Volume
187
Numéro
1
Pages
43-56
Langue
anglais
Notes
Monfort_Renne_Roussellet_2015
Résumé
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.
Mots-clé
Non-linear filtering, Non-linear smoothing, Quadratic model, Kalman filter, Quasi maximum likelihood
Web of science
Création de la notice
23/09/2015 16:46
Dernière modification de la notice
03/03/2018 21:41
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