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Constructing stationary time series models using auxiliary variables with applications
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UNSPECIFIED (2005) Constructing stationary time series models using auxiliary variables with applications. JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 100 (470). pp. 554-564. doi:10.1198/016214504000001970 ISSN 0162-1459.
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Official URL: http://dx.doi.org/10.1198/016214504000001970
Abstract
Here we present a novel method for modeling stationary time series. Our approach is to construct the model with a specified marginal family and build the dependence structure around it. We show that the resulting time series is linear with a simple autocorrelation structure. We construct models that parallel existing structures, namely state-space models, autoregressive conditional heteroscedasticity (ARCH) models, and generalized ARCH models. We use Bayesian techniques to estimate the resulting models. We also demonstrate that the models perform well compared with competing methods for the applications considered, count models and volatility models.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Journal or Publication Title: | JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION | ||||
Publisher: | AMER STATISTICAL ASSOC | ||||
ISSN: | 0162-1459 | ||||
Official Date: | June 2005 | ||||
Dates: |
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Volume: | 100 | ||||
Number: | 470 | ||||
Number of Pages: | 11 | ||||
Page Range: | pp. 554-564 | ||||
DOI: | 10.1198/016214504000001970 | ||||
Publication Status: | Published |
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