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TIME-SERIES MODELS WITH AN EGB2 CONDITIONAL DISTRIBUTION


Type

Article

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Authors

Caivano, Michele 
Harvey, Andrew 

Abstract

jats:pA time‐series model in which the signal is buried in noise that is non‐Gaussian may throw up observations that, when judged by the Gaussian yardstick, are outliers. We describe an observation‐driven model, based on an exponential generalized beta distribution of the second kind (EGB2), in which the signal is a linear function of past values of the score of the conditional distribution. This specification produces a model that is not only easy to implement but which also facilitates the development of a comprehensive and relatively straightforward theory for the asymptotic distribution of the maximum‐likelihood (ML) estimator. Score‐driven models of this kind can also be based on conditional jats:italict</jats:italic> distributions, but whereas these models carry out what, in the robustness literature, is called a soft form of trimming, the EGB2 distribution leads to a soft form of Winsorizing. An exponential general autoregressive conditional heteroscedastic (EGARCH) model based on the EGB2 distribution is also developed. This model complements the score‐driven EGARCH model with a conditional jats:italict</jats:italic> distribution. Finally, dynamic location and scale models are combined and applied to data on the UK rate of inflation.</jats:p>

Description

Keywords

Beta distribution, EGARCH, outlier, robustness, score, WinsorizingJEL, C22l, G17

Journal Title

JOURNAL OF TIME SERIES ANALYSIS

Conference Name

Journal ISSN

0143-9782
1467-9892

Volume Title

Publisher

Wiley

Rights

DSpace@Cambridge license