Beyond Location and Dispersion Models: The Generalized Structural Time Series Model with Applications

Published: Jan 1, 2015
Abstract
In many settings of empirical interest, time variation in the distribution parameters is important for capturing the dynamic behaviour of time series processes. Although the fitting of heavy tail distributions has become easier due to computational advances, the joint and explicit modelling of time-varying conditional skewness and kurtosis is a challenging task. We propose a class of parameter-driven time series models referred to as the...
Paper Details
Title
Beyond Location and Dispersion Models: The Generalized Structural Time Series Model with Applications
Published Date
Jan 1, 2015
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