Recursive Residuals and Model Diagnostics for Normal and Non-Normal State Space Models

Sylvia Frühwirth-Schnatter

doi:10.57938/469a9e44-ed5e-4e96-9004-206f0b9545fd

Recursive Residuals and Model Diagnostics for Normal and Non-Normal State Space Models

Sylvia Frühwirth-Schnatter

Statistics and Mathematics

Publikation: Working/Discussion Paper › WU Working Paper

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Abstract

Model diagnostics for normal and non-normal state space models is based on recursive residuals which are defined from the one-step ahead predictive distribution. Routine calculation of these residuals is discussed in detail. Various tools of diagnostics are suggested to check e.g. for wrong observation distributions and for autocorrelation. The paper also covers such topics as model diagnostics for discrete time series, model diagnostics for generalized linear models, and model discrimination via Bayes factors.

Originalsprache	Englisch
Erscheinungsort	Vienna
Herausgeber	Department of Statistics and Mathematics, WU Vienna University of Economics and Business
DOIs	https://doi.org/10.57938/469a9e44-ed5e-4e96-9004-206f0b9545fd
Publikationsstatus	Veröffentlicht - 1994

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Reihe	Forschungsberichte / Institut für Statistik
Nummer	40

WU Working Paper Reihe

Forschungsberichte / Institut für Statistik

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1 Originalbeitrag in Fachzeitschrift

Recursive residuals and model diagnostics for normal and nonnormal state space models
Frühwirth-Schnatter, S., 1996, in: Environmental and Ecological Statistics. 3, S. 291 - 309
Publikation: Wissenschaftliche Fachzeitschrift › Originalbeitrag in Fachzeitschrift › Begutachtung

Zitat

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Recursive Residuals and Model Diagnostics for Normal and Non-Normal State Space Models

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Recursive residuals and model diagnostics for normal and nonnormal state space models

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