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Pointwise and functional approximations in Monte Carlo maximum likelihood estimation

✍ Scribed by Anthony Y. C. Kuk; Yuk W. Cheng

Book ID
110267842
Publisher
Springer US
Year
1999
Tongue
English
Weight
171 KB
Volume
9
Category
Article
ISSN
0960-3174

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πŸ“œ SIMILAR VOLUMES

The use of approximating models in Monte
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✍ Anthony Y.C Kuk πŸ“‚ Article πŸ“… 1999 πŸ› Elsevier Science 🌐 English βš– 102 KB

To obtain the likelihood of a non-Gaussian state-space model, Durbin and Koopman (1997, Biometrika, 84, 669 -684) ΓΏrst calculate the likelihood under an approximating linear Gaussian model and then use Monte Carlo methods to estimate the necessary adjustment factor. We show that Durbin and Koopman's

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Maximum likelihood estimation of STAR and STAR-GARCH models: theory and Monte Carlo evidence
✍ Felix Chan; Michael McAleer πŸ“‚ Article πŸ“… 2002 πŸ› John Wiley and Sons 🌐 English βš– 223 KB

## Abstract Theoretical and practical interest in non‐linear time series models, particularly regime switching models, have increased substantially in recent years. Given the abundant research activity in analysing time‐varying volatility through Generalized Autoregressive Conditional Heteroscedast