Constructing Non-linear Gaussian Time Series by Means of a Simplified State Space Representation
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| 024 | 7 | 0 | |a 10.2202/1558-3708.1213 |2 doi |
| 035 | |a (NATIONALLICENCE)gruyter-10.2202/1558-3708.1213 | ||
| 100 | 1 | |a Vidoni |D Paolo |u 1University of Udine, vidoni@dss.uniud.it | |
| 245 | 1 | 0 | |a Constructing Non-linear Gaussian Time Series by Means of a Simplified State Space Representation |h [Elektronische Daten] |c [Paolo Vidoni] |
| 520 | 3 | |a State space models provide a useful stochastic description for dynamic phenomena, based on unobserved or latent variables. When the model rests on linear and Gaussian assumptions there exists a well-known iterative procedure, called the Kalman filter, which gives analytic updating recursion for the filtering, the prediction and the smoothing distributions. However, this is rare and a state space model does not usually admit such a filter. For this reason, instead of looking for analytic solutions, a number of papers aim to define alternative procedures, giving numerical or approximate solutions. This paper concerns a particular class of models based on the assumption that the mixed process, obtained by alternating states and observations, is a Markov process. The main features of this class of models, proposed for stochastic volatility description by Barndorff-Nielsen (1997), are emphasized. In this framework, some new non-linear Gaussian state space models, computationally tractable and of potential interest for applications, may be defined. | |
| 540 | |a ©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston | ||
| 773 | 0 | |t Studies in Nonlinear Dynamics & Econometrics |d De Gruyter |g 8/2(2004-05-18) |q 8:2 |1 2004 |2 8 |o snde | |
| 856 | 4 | 0 | |u https://doi.org/10.2202/1558-3708.1213 |q text/html |z Onlinezugriff via DOI |
| 908 | |D 1 |a research article |2 jats | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.2202/1558-3708.1213 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 100 |E 1- |a Vidoni |D Paolo |u 1University of Udine, vidoni@dss.uniud.it | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Studies in Nonlinear Dynamics & Econometrics |d De Gruyter |g 8/2(2004-05-18) |q 8:2 |1 2004 |2 8 |o snde | ||
| 900 | 7 | |b CC0 |u http://creativecommons.org/publicdomain/zero/1.0 |2 nationallicence | |
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