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Statistics seminar 2018: A General Class of Score-Driven Smoothers

12/07/2018 dalle 14:30 alle 16:30

Dove Dipartimento di Scienze Statistiche - via delle Belle Arti 41 - Aula Seminari

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Relatore
Giacomo Bormetti
Dipartimento di Matematica - Università di Bologna

Abstract
By interpreting score-driven models of Creal et al. (2013) and Harvey (2013) as approximate filters, we introduce a new class of simple approximate smoothers for nonlinear non-Gaussian state-space models that are named "Score-Driven Smoothers" (SDS). The newly proposed SDS improves on standard score-driven filtered estimates, as it employs all available observations. In contrast to complex simulations-based methods, the SDS has similar structure to Kalman backward smoothing recursions but uses the score of the non-Gaussian density. Through an extensive Monte Carlo study, we provide evidence that the performance of the approximation is very close to that of simulation-based techniques, while at the same time requiring significantly lower computational burden.

Joint work with Giuseppe Buccheri, Fulvio Corsi, and Fabrizio Lillo

Organizzazione
Monia Lupparelli