کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7408384 1481440 2015 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Copula modelling of dependence in multivariate time series
ترجمه فارسی عنوان
مدل سازی کوپول وابستگی در سری زمانی چند متغیره
کلمات کلیدی
مدل کاپولا، سری زمانی چند متغیره غیر خطی، میانگین بیزی مدل، استقرار چند متغیره،
موضوعات مرتبط
علوم انسانی و اجتماعی مدیریت، کسب و کار و حسابداری کسب و کار و مدیریت بین المللی
چکیده انگلیسی
Almost all existing nonlinear multivariate time series models remain linear, conditional on a point in time or latent regime. Here, an alternative is proposed, where nonlinear serial and cross-sectional dependence is captured by a copula model. The copula defines a multivariate time series on the unit cube. A drawable vine copula is employed, along with a factorization which allows the marginal and transitional densities of the time series to be expressed analytically. The factorization also provides for simple conditions under which the series is stationary and/or Markov, as well as being parsimonious. A parallel algorithm for computing the likelihood is proposed, along with a Bayesian approach for computing inference based on model averages over parsimonious representations of the vine copula. The model average estimates are shown to be more accurate in a simulation study. Two five-dimensional time series from the Australian electricity market are examined. In both examples, the fitted copula captures a substantial level of asymmetric tail dependence, both over time and between elements in the series.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Forecasting - Volume 31, Issue 3, July–September 2015, Pages 815-833
نویسندگان
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