Estimation and Inference for Heavy-Tailed Threshold Time Series Models
Başlık:
Estimation and Inference for Heavy-Tailed Threshold Time Series Models
Yazar:
Yang, Yaxing, author.
ISBN:
9780438131460
Yazar Ek Girişi:
Fiziksel Tanımlama:
1 electronic resource (148 pages)
Genel Not:
Source: Masters Abstracts International, Volume: 57-06M(E).
Özet:
This thesis develops the systematic procedures of statistical inference for the heavy-tailed threshold autoregressive (TAR) models and heavy-tailed multiple threshold double autoregressive (MTDAR) models, respectively. The asymptotic theory of the self-weighted least absolute deviation (SLAD) estimation for the TAR models and quasi-maximum exponential likelihood estimation (QMELE) for the MTDAR models are obtained. Since the objective functions of these two models involves the discontinuous threshold parameters, the arguments in this thesis are more demanding than the usual LAD estimation. To our knowledge, this is the first attempt to study the asymptotic theory for the estimation of the heavy-tailed threshold time series in the literature. This thesis also investigates the large sample theory of quasi-maximum likelihood estimator (QMLE) for a threshold GARCH model with unknown threshold parameter. Three empirical examples are given to illustrate the usefulness of these threshold models. All of these results are new and bridge a gap in the statistical literature of nonlinear time series.
Notlar:
School code: 1223
Konu Başlığı:
Tüzel Kişi Ek Girişi:
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Yer Numarası | Demirbaş Numarası | Shelf Location | Lokasyon / Statüsü / İade Tarihi |
---|---|---|---|
XX(696845.1) | 696845-1001 | Proquest E-Tez Koleksiyonu | Arıyor... |
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