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