A Comparison of Two Alternative Approaches to Modeling Level Shifts in the Presence of Outliers

We study alternative models for capturing abrupt structural changes (level shifts) in a times series. The problem is confounded by the presence of transient outliers. We compare the performance of non-Gaussian time-varying parameter models and multiprocess mixture models within a Monte Carlo experim...

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Bibliographic Details
Published in:Communications in statistics. Simulation and computation 2004-01, Vol.33 (3), p.661-671
Main Author: Bidarkota, Prasad V.
Format: Article
Language:English
Subjects:
Distribution theory
Exact sciences and technology
Inference from stochastic processes
time series analysis
Level shifts
Mathematics
Multiprocess mixture models
Multivariate analysis
Non-Gaussian state space models
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in probability and statistics
Outliers
Primary 91B84, 62M10, 60G35
Probability and statistics
Sciences and techniques of general use
Secondary 62P20, 93E11
Statistics
Time-varying parameter (TVP) models
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Summary:We study alternative models for capturing abrupt structural changes (level shifts) in a times series. The problem is confounded by the presence of transient outliers. We compare the performance of non-Gaussian time-varying parameter models and multiprocess mixture models within a Monte Carlo experimental setup. Our findings suggest that once we incorporate shocks with thick-tailed probability distributions, the superiority of the multiprocess mixture models over the time-varying parameter models, reported in an earlier study, disappears. The behavior of the two models, both in adapting to level shifts and in reacting to transient outliers, is very similar.
ISSN:0361-0918
1532-4141
DOI:10.1081/SAC-200033317