摘要翻译：
本文旨在提出一种预测多元时间序列的时变向量自回归模型（TV-VAR）。该模型被浇铸成一种状态空间形式，允许灵活的描述和分析。时间序列的波动协方差矩阵是通过倒Wishart分布和奇异多元贝塔分布来建模的，允许完全共轭贝叶斯推理。通过似然函数、序贯Bayes因子、标准预测误差平方均值、绝对预测误差均值（也称为平均绝对偏差）和平均预测误差进行模型性能和模型比较。为了选择模型的自回归阶数，还使用了贝叶斯因子。详细讨论了多步预测，并提出了一个灵活的预测函数逼近公式。两个例子，包括IBM股票和8种货币的外汇汇率的双变量数据，说明了这些方法。对于IBM数据，我们详细讨论了模型性能和多步预测。对于外汇数据，我们讨论了顺序投资组合配置；对于这两个数据集，我们的实证结果表明TV-VAR模型优于广泛使用的VAR模型。
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英文标题：
《Forecasting with time-varying vector autoregressive models》
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作者：
K. Triantafyllopoulos
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最新提交年份：
2008
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Statistics        统计学
二级分类：Methodology        方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
  The purpose of this paper is to propose a time-varying vector autoregressive model (TV-VAR) for forecasting multivariate time series. The model is casted into a state-space form that allows flexible description and analysis. The volatility covariance matrix of the time series is modelled via inverted Wishart and singular multivariate beta distributions allowing a fully conjugate Bayesian inference. Model performance and model comparison is done via the likelihood function, sequential Bayes factors, the mean of squared standardized forecast errors, the mean of absolute forecast errors (known also as mean absolute deviation), and the mean forecast error. Bayes factors are also used in order to choose the autoregressive order of the model. Multi-step forecasting is discussed in detail and a flexible formula is proposed to approximate the forecast function. Two examples, consisting of bivariate data of IBM shares and of foreign exchange (FX) rates for 8 currencies, illustrate the methods. For the IBM data we discuss model performance and multi-step forecasting in some detail. For the FX data we discuss sequential portfolio allocation; for both data sets our empirical findings suggest that the TV-VAR models outperform the widely used VAR models.
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PDF链接：
https://arxiv.org/pdf/0802.0220